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Jaafari A, Panahi M, Mafi-Gholami D, Rahmati O, Shahabi H, Shirzadi A, Lee S, Bui DT, Pradhan B. Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108254] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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2
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Phan DC, Trung TH, Truong VT, Sasagawa T, Vu TPT, Bui DT, Hayashi M, Tadono T, Nasahara KN. First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam. Sci Rep 2021; 11:9979. [PMID: 33976255 PMCID: PMC8113344 DOI: 10.1038/s41598-021-89034-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 04/20/2021] [Indexed: 02/03/2023] Open
Abstract
Extensive studies have highlighted a need for frequently consistent land cover information for interdisciplinary studies. This paper proposes a comprehensive framework for the automatic production of the first Vietnam-wide annual land use/land cover (LULC) data sets (VLUCDs) from 1990 to 2020, using available remotely sensed and inventory data. Classification accuracies ranged from 85.7 ± 1.3 to 92.0 ± 1.2% with the primary dominant LULC and 77.6 ± 1.2% to 84.7 ± 1.1% with the secondary dominant LULC. This confirmed the potential of the proposed framework for systematically long-term monitoring LULC in Vietnam. Results reveal that despite slight recoveries in 2000 and 2010, the net loss of forests (19,940 km2) mainly transformed to croplands over 30 years. Meanwhile, productive croplands were converted to urban areas, which increased approximately ten times. A threefold increase in aquaculture was a major driver of the wetland loss (1914 km2). The spatial-temporal changes varied, but the most dynamic regions were the western north, the southern centre, and the south. These findings can provide evidence-based information on formulating and implementing coherent land management policies. The explicitly spatio-temporal VLUCDs can be benchmarks for global LULC validation, and utilized for a variety of applications in the research of environmental changes towards the Sustainable Development Goals.
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Affiliation(s)
- Duong Cao Phan
- Graduate School of Science and Technology, University of Tsukuba, Tennoudai 1-1-1, Tsukuba, Ibaraki, 305-8572, Japan.
- Hydraulic Construction Institute, Vietnam Academy for Water Resources, No. 3, Alley 95, Chua Boc Street, Dong Da district, Hanoi, 116765, Vietnam.
| | - Ta Hoang Trung
- Department of Survey, Mapping and Geographic Information, Ministry of Natural Resources and Environment, 2 Dang Thuy Tram Street, Hanoi, 100000, Vietnam
| | - Van Thinh Truong
- VNU Center for Development in Hoa Lac, Vietnam National University, Hanoi, Thach Hoa Commune, Thach That District, Hanoi, 155500, Vietnam
| | - Taiga Sasagawa
- Graduate School of Science and Technology, University of Tsukuba, Tennoudai 1-1-1, Tsukuba, Ibaraki, 305-8572, Japan
| | - Thuy Phuong Thi Vu
- Forest Inventory and Planning Institute (FIPI), Ministry of Agriculture and Rural Development (MARD), Vinh Quynh, Thanh Tri, Hanoi, 100000, Vietnam
| | - Dieu Tien Bui
- GIS Group, Department of Business and IT, University of South-Eastern Norway, Gullbringvegen 36, N-3800, Bø i Telemark, Norway
| | - Masato Hayashi
- Earth Observation Research Center, Japan Aerospace Exploration Agency (JAXA), 2-1-1 Sengen, Tsukuba, Ibaraki, 305-8505, Japan
| | - Takeo Tadono
- Earth Observation Research Center, Japan Aerospace Exploration Agency (JAXA), 2-1-1 Sengen, Tsukuba, Ibaraki, 305-8505, Japan
| | - Kenlo Nishida Nasahara
- Faculty of Life and Environmental Sciences, University of Tsukuba, Tennoudai 1-1-1, Tsukuba, Ibaraki, 305-8572, Japan
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Ngo PTT, Pham TD, Hoang ND, Tran DA, Amiri M, Le TT, Hoa PV, Bui PV, Nhu VH, Bui DT. A new hybrid equilibrium optimized SysFor based geospatial data mining for tropical storm-induced flash flood susceptible mapping. J Environ Manage 2021; 280:111858. [PMID: 33360552 DOI: 10.1016/j.jenvman.2020.111858] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 12/08/2020] [Accepted: 12/13/2020] [Indexed: 06/12/2023]
Abstract
Flash flood is one of the most dangerous hydrologic and natural phenomena and is considered as the top ranking of such events among various natural disasters due to their fast onset characteristics and the proportion of individual fatalities. Mapping the probability of flash flood events remains challenges because of its complexity and rapid onset of precipitation. Thus, this study aims to propose a state-of-the-art data mining approach based on a hybrid equilibrium optimized SysFor, namely, the HE-SysFor model, for spatial prediction of flash floods. A tropical storm region located in the Northwest areas of Vietnam is selected as a case study. For this purpose, 1866 flash-flooded locations and ten indicators were used. The results show that the proposed HE-SysFor model yielded the highest predictive performance (total accuracy = 93.8%, Kappa index = 0.875, F1-score = 0.939, and AUC = 0.975) and produced the better performance than those of the C4.5 decision tree (C4.5), the radial basis function-based support vector machine (SVM-RBF), the logistic regression (LReg), and deep learning neural network (DeepLNN) models in both the training and the testing phases. Among the ten indicators, elevation, slope, and land cover are the most important. It is concluded that the proposed model provides an alternative tool and may help for effectively monitoring flash floods in tropical areas and robust policies for decision making in mitigating the flash flood impacts.
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Affiliation(s)
- Phuong-Thao Thi Ngo
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam
| | - Tien Dat Pham
- Center for Agricultural Research and Ecological Studies (CARES), Vietnam National University of Agriculture, Trau Quy, Gia Lam, Hanoi, 100000, Viet Nam
| | - Nhat-Duc Hoang
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam; Faculty of Civil Engineering, Duy Tan University, P809 - 03 Quang Trung, Da Nang, 550000, Viet Nam
| | - Dang An Tran
- Faculty of Water Resources Engineering, Thuyloi University, 175 Tay Son, Dong Da, Ha Noi, 100000, Viet Nam
| | - Mahdis Amiri
- Department of Watershed & Arid Zone Management, Gorgan University of Agricultural Sciences & Natural Resources, Gorgan, 4918943464, Iran
| | - Thu Trang Le
- Laboratoire Magmas et Volcans, Université Clermont Auvergne, CNRS, IRD, OPGC, F-63000, Clermont-Ferrand, France
| | - Pham Viet Hoa
- Ho Chi Minh City Institute of Resources Geography, Vietnam Academy of Science and Technology, Mac Dinh Chi 1, Ben Nghe, 1 District, Ho Chi Minh City, 700000, Viet Nam
| | - Phong Van Bui
- Department of Hydrogeology and Engineering Geology, Vietnam Institute of Geosciences and Mineral Resources (VIGMR), Viet Nam
| | - Viet-Ha Nhu
- Department of Geological-Geotechnical Engineering, Hanoi University of Mining and Geology, Hanoi, Viet Nam
| | - Dieu Tien Bui
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; GIS Group, Department of Business and IT, University of South-Eastern Norway, N-3800 Bø i Telemark, Norway.
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Arora A, Arabameri A, Pandey M, Siddiqui MA, Shukla UK, Bui DT, Mishra VN, Bhardwaj A. Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India. Sci Total Environ 2021; 750:141565. [PMID: 32882492 DOI: 10.1016/j.scitotenv.2020.141565] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 07/31/2020] [Accepted: 08/06/2020] [Indexed: 05/22/2023]
Abstract
This study is an attempt to quantitatively test and compare novel advanced-machine learning algorithms in terms of their performance in achieving the goal of predicting flood susceptible areas in a low altitudinal range, sub-tropical floodplain environmental setting, like that prevailing in the Middle Ganga Plain (MGP), India. This part of the Ganga floodplain region, which under the influence of undergoing active tectonic regime related subsidence, is the hotbed of annual flood disaster. This makes the region one of the best natural laboratories to test the flood susceptibility models for establishing a universalization of such models in low relief highly flood prone areas. Based on highly sophisticated flood inventory archived for this region, and 12 flood conditioning factors viz. annual rainfall, soil type, stream density, distance from stream, distance from road, Topographic Wetness Index (TWI), altitude, slope aspect, slope, curvature, land use/land cover, and geomorphology, an advanced novel hybrid model Adaptive Neuro Fuzzy Inference System (ANFIS), and three metaheuristic models-based ensembles with ANFIS namely ANFIS-GA (Genetic Algorithm), ANFIS-DE (Differential Evolution), and ANFIS-PSO (Particle Swarm Optimization), have been applied for zonation of the flood susceptible areas. The flood inventory dataset, prepared by collected flood samples, were apportioned into 70:30 classes to prepare training and validation datasets. One independent validation method, the Area-Under Receiver Operating Characteristic (AUROC) Curve, and other 11 cut-off-dependent model evaluation metrices have helped to conclude that the ANIFS-GA has outperformed other three models with highest success rate AUC = 0.922 and prediction rate AUC = 0.924. The accuracy was also found to be highest for ANFIS-GA during training (0.886) & validation (0.883). Better performance of ANIFS-GA than the individual models as well as some ensemble models suggests and warrants further study in this topoclimatic environment using other classes of susceptibility models. This will further help establishing a benchmark model with capability of highest accuracy and sensitivity performance in the similar topographic and climatic setting taking assumption of the quality of input parameters as constant.
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Affiliation(s)
- Aman Arora
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India.
| | - Alireza Arabameri
- Department of Geomorphology, Tarbiat Modares University, Jalal Ale Ahmad Highway, Tehran 9821, Iran
| | - Manish Pandey
- University Center for Research & Development (UCRD), Chandigarh University, Mohali 140413, Punjab, India; Department of Civil Engineering, Chandigarh University, Mohali 140413, Punjab, India.
| | - Masood A Siddiqui
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - U K Shukla
- Center for Advanced Study in Geology, Institute of Science, Banaras Hindu University, Varanasi 221005, India
| | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam
| | - Varun Narayan Mishra
- Centre for Climate Change and Water Research, Suresh Gyan Vihar University, Jaipur 302017, Rajasthan, India
| | - Anshuman Bhardwaj
- School of Geosciences, University of Aberdeen, Meston Building, King's College, Aberdeen AB24 3UE, UK; Division of Space Technology, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, Sweden
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Gholami H, Mohammadifar A, Bui DT, Collins AL. Mapping wind erosion hazard with regression-based machine learning algorithms. Sci Rep 2020; 10:20494. [PMID: 33235269 PMCID: PMC7686346 DOI: 10.1038/s41598-020-77567-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 11/10/2020] [Indexed: 11/09/2022] Open
Abstract
Land susceptibility to wind erosion hazard in Isfahan province, Iran, was mapped by testing 16 advanced regression-based machine learning methods: Robust linear regression (RLR), Cforest, Non-convex penalized quantile regression (NCPQR), Neural network with feature extraction (NNFE), Monotone multi-layer perception neural network (MMLPNN), Ridge regression (RR), Boosting generalized linear model (BGLM), Negative binomial generalized linear model (NBGLM), Boosting generalized additive model (BGAM), Spline generalized additive model (SGAM), Spike and slab regression (SSR), Stochastic gradient boosting (SGB), support vector machine (SVM), Relevance vector machine (RVM) and the Cubist and Adaptive network-based fuzzy inference system (ANFIS). Thirteen factors controlling wind erosion were mapped, and multicollinearity among these factors was quantified using the tolerance coefficient (TC) and variance inflation factor (VIF). Model performance was assessed by RMSE, MAE, MBE, and a Taylor diagram using both training and validation datasets. The result showed that five models (MMLPNN, SGAM, Cforest, BGAM and SGB) are capable of delivering a high prediction accuracy for land susceptibility to wind erosion hazard. DEM, precipitation, and vegetation (NDVI) are the most critical factors controlling wind erosion in the study area. Overall, regression-based machine learning models are efficient techniques for mapping land susceptibility to wind erosion hazards.
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Affiliation(s)
- Hamid Gholami
- Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran.
| | - Aliakbar Mohammadifar
- Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran
| | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam.
- GIS Group, Department of Business and IT, University of South-Eastern Norway, 3800, Bø i Telemark, Norway.
| | - Adrian L Collins
- Sustainable Agriculture Sciences, Rothamsted/Research, North Wyke, Okehampton, EX20 2SB, Devon, UK
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Mafi-Gholami D, Jaafari A, Zenner EK, Nouri Kamari A, Tien Bui D. Vulnerability of coastal communities to climate change: Thirty-year trend analysis and prospective prediction for the coastal regions of the Persian Gulf and Gulf of Oman. Sci Total Environ 2020; 741:140305. [PMID: 32887018 DOI: 10.1016/j.scitotenv.2020.140305] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 06/01/2020] [Accepted: 06/15/2020] [Indexed: 06/11/2023]
Abstract
This study relates changes in social vulnerability of 20 counties on the northern coasts of the Persian Gulf (PG) and the Gulf of Oman (GO) over a 30-year period (1988-2017) to changing socio-economic conditions and environmental (climate) hazard. Social vulnerability in 2030, 2040 and 2050 is predicted based on the RCP8.5 climate change scenario that projects drought intensities and rising sea levels. Social vulnerability was based on the three dimensions of sensitivity, exposure, and adaptive capacity using 18 socio-economic and five climate indicators identified by experts. All but one indicator related very strongly to the dimension it sought to represent. Despite improvements in adaptive capacity over time, social vulnerability increased between 1988 and 2017 and rates of change accelerated after change point years that occurred between 1998 and 2002 in most counties. Extrapolating past changes of each indicator over time enabled forecasts of social vulnerability in the future. While social variability decreased between 2017 and 2030, it increased again between 2030 and 2050. The lowest future social vulnerability is expected along the eastern PG coast, the greatest along the western PG and the GO. The worsening of socio-economic indicators contributed to increased sensitivity, and increased drought intensities plus the expected rise in sea levels will lead to social vulnerabilities in 2050 comparable to present levels. Between 1.4 and 1.7 M people will live in areas that are likely submerged by water in the future. About 80% of these people live in six counties with variable social vulnerabilities. While counties with lower social variabilities might be better able to cope with the challenges posed by climate change, adaptation programs to enhance the resilience of the residents in these and the remaining counties along the PG and the GO need to be implemented soon to avoid uncontrolled mass migration of millions of people from the region.
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Affiliation(s)
- Davood Mafi-Gholami
- Department of Forest Sciences, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, Iran.
| | - Abolfazl Jaafari
- Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.
| | - Eric K Zenner
- Department of Ecosystem Science and Management, The Pennsylvania State University, Forest Resources Building, University Park, PA 16802, USA.
| | - Akram Nouri Kamari
- Department of Environment, Faculty of Natural Resource, University of Tehran, Tehran, Iran.
| | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam.
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Mafi-Gholami D, Jaafari A, Zenner EK, Nouri Kamari A, Tien Bui D. Spatial modeling of exposure of mangrove ecosystems to multiple environmental hazards. Sci Total Environ 2020; 740:140167. [PMID: 32569915 DOI: 10.1016/j.scitotenv.2020.140167] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 05/31/2020] [Accepted: 06/10/2020] [Indexed: 05/18/2023]
Abstract
Determining the level of ecosystems exposure to multiple environmental hazards or risk factors is of paramount importance for developing, adopting, and planning management strategies to minimize the harmful effects of these hazards. We quantified the level of exposure of mangroves on the northern coasts of the Persian Gulf (PG) and the Gulf of Oman (GO) between 1986 and 2019 to eight environmental hazards, i.e., drought, maximum temperatures, rising sea levels, change of freshwater inflows to coasts, extreme storm surges, significant wave height (SWH), seaward edge retreat in the mangroves, and fishery intensity. Based on expert opinion, fuzzy weights were used to integrate these exposures into a single index (EI) for the region. Experts gave the greatest weight/importance to the risks posed by sea-level rise and seaward retreat of mangroves and the lowest risk to significant wave height and fishery intensity in coastal waters. The overall EI and six of eight individual variables (except fishery intensity and maximum temperatures) pointed to exposure levels of mangroves that increased from the coasts of the PG (EI 0.69) to the GO (EI 6.69). Since these hazards are expected to continue in the future, local/regional management responses should focus on minimizing regional anthropogenic threats and halt conversion of natural areas to agricultural and open areas to maintain freshwater inputs to coastal areas, particularly on the GO. Further, uplands that may serve as future refugia into which mangroves may expand over time as sea levels continue to rise should be protected from development. This was the first study that used an analytic framework to compute a mangrove exposure index to a suite of physical and socio-economic hazards across a region. This framework may provide insights into cost-effective resilience-based design and management of socio-ecologically coupled ecosystems in an era of increasing types and intensities of environmental hazards.
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Affiliation(s)
- Davood Mafi-Gholami
- Department of Forest Sciences, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, Iran.
| | - Abolfazl Jaafari
- Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.
| | - Eric K Zenner
- Department of Ecosystem Science and Management, The Pennsylvania State University, Forest Resources Building, University Park, PA 16802, USA.
| | - Akram Nouri Kamari
- Department of Environment, Faculty of Natural Resource, University of Tehran, Tehran, Iran.
| | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam.
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Rahmati O, Mohammadi F, Ghiasi SS, Tiefenbacher J, Moghaddam DD, Coulon F, Nalivan OA, Tien Bui D. Identifying sources of dust aerosol using a new framework based on remote sensing and modelling. Sci Total Environ 2020; 737:139508. [PMID: 32531509 DOI: 10.1016/j.scitotenv.2020.139508] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 05/11/2020] [Accepted: 05/16/2020] [Indexed: 05/23/2023]
Abstract
Dust particles are transported globally. Dust storms can adversely impact both human health and the environment, but they also impact transportation infrastructure, agriculture, and industry, occasionally severely. The identification of the locations that are the primary sources of dust, especially in arid and semi-arid environments, remains a challenge as these sites are often in remote or data-scarce regions. In this study, a new method using state-of-the-art machine-learning algorithms - random forest (RF), support vector machines (SVM), and multivariate adaptive regression splines (MARS) - was evaluated for its ability to spatially model the distribution of dust-source potential in eastern Iran. To accomplish this, empirically identified dust-source locations were determined with the ozone monitoring instrument aerosol index and the Moderate-Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol optical thickness methods. The identified areas were divided into training (70%) and validation (30%) sets. Measurements of the conditioning factors (lithology, wind speed, maximum air temperature, land use, slope angle, soil, rainfall, and land cover) were compiled for the study area and predictive models were developed. The area-under-the-receiver operating characteristics curve (AUC) and true-skill statistics (TSS) were used to validate the maps of the models' predictions. The results show that the RF algorithm performed best (AUC = 89.4% and TSS = 0.751), followed by the SVM (AUC = 87.5%, TSS = 0.73) and the MARS algorithm (AUC = 81%, TSS = 0.69). The results of the RF indicated that wind speed and land cover are the most important factors affecting dust generation. The region of highest dust-source potential that was identified by the RF is in the eastern parts of the study region. This model can be applied to other arid and semi-arid environments that experience dust storms to promote management that prevents desertification and reduces dust production.
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Affiliation(s)
- Omid Rahmati
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
| | - Farnoush Mohammadi
- Faculty of Natural Resources Management, University of Tehran, Karaj, Iran
| | - Seid Saeid Ghiasi
- Faculty of Natural Resources Management, University of Tehran, Karaj, Iran
| | - John Tiefenbacher
- Department of Geography, Texas State University, San Marcos, TX 78666, USA
| | - Davoud Davoudi Moghaddam
- Department of Watershed Management, Agriculture and Natural Resources Faculty, Lorestan University, Khorramabad, Iran
| | - Frederic Coulon
- School of Water, Energy and Environment, Cranfield University, Cranfield MK43 0AL, UK
| | - Omid Asadi Nalivan
- Department of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
| | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam.
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Priyadarshini I, Mohanty P, Kumar R, Son LH, Chau HTM, Nhu VH, Thi Ngo PT, Tien Bui D. Analysis of Outbreak and Global Impacts of the COVID-19. Healthcare (Basel) 2020; 8:E148. [PMID: 32485875 PMCID: PMC7349011 DOI: 10.3390/healthcare8020148] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 04/19/2020] [Accepted: 04/21/2020] [Indexed: 12/15/2022] Open
Abstract
Corona viruses are a large family of viruses that are not only restricted to causing illness in humans but also affect animals such as camels, cattle, cats, and bats, thus affecting a large group of living species. The outbreak of Corona virus in late December 2019 (also known as COVID-19) raised major concerns when the outbreak started getting tremendous. While the first case was discovered in Wuhan, China, it did not take long for the disease to travel across the globe and infect every continent (except Antarctica), killing thousands of people. Since it has become a global concern, different countries have been working toward the treatment and generation of vaccine, leading to different speculations. While some argue that the vaccine may only be a few weeks away, others believe that it may take some time to create the vaccine. Given the increasing number of deaths, the COVID-19 has caused havoc worldwide and is a matter of serious concern. Thus, there is a need to study how the disease has been propagating across continents by numbers as well as by regions. This study incorporates a detailed description of how the COVID-19 outbreak started in China and managed to spread across the globe rapidly. We take into account the COVID-19 outbreak cases (confirmed, recovered, death) in order to make some observations regarding the pandemic. Given the detailed description of the outbreak, this study would be beneficial to certain industries that may be affected by the outbreak in order to take timely precautionary measures in the future. Further, the study lists some industries that have witnessed the impact of the COVID-19 outbreak on a global scale.
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Affiliation(s)
- Ishaani Priyadarshini
- Department of Electrical and Computer Science, University of Delaware, Newark, DE 19716, USA;
| | - Pinaki Mohanty
- Department of Computer Science, Purdue University, 610 Purdue Mall, West Lafayette, IN 47907, USA;
| | - Raghvendra Kumar
- Computer Science and Engineering Department, GIET University, Gunupur, Odisha 765022, India;
| | - Le Hoang Son
- VNU Information Technology Institute, Vietnam National University, Hanoi 010000, Vietnam;
| | - Hoang Thi Minh Chau
- Faculty of Information Technology, University of Economic and Technical Industries, Hanoi 010000, Vietnam;
| | - Viet-Ha Nhu
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
- Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
| | - Phuong Thao Thi Ngo
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam;
| | - Dieu Tien Bui
- Department of Business and IT, University of South-Eastern Norway, Gullbringvegen 36, N-3800 BøiTelemark, Norway;
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Rahmati O, Panahi M, Kalantari Z, Soltani E, Falah F, Dayal KS, Mohammadi F, Deo RC, Tiefenbacher J, Tien Bui D. Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia. Sci Total Environ 2020; 718:134656. [PMID: 31839310 DOI: 10.1016/j.scitotenv.2019.134656] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 09/23/2019] [Accepted: 09/24/2019] [Indexed: 06/10/2023]
Abstract
Widespread detrimental and long-lasting droughts are having catastrophic impacts around the globe. Researchers, organizations, and policy makers need to work together to obtain precise information, enabling timely and accurate decision making to mitigate drought impacts. In this study, a spatial modeling approach based on an adaptive neuro-fuzzy inference system (ANFIS) and several metaheuristic optimizations (ANFIS-BA, ANFIS-GA, ANFIS-ICA, ANFIS-PSO) was developed to predict the spatial occurrence of drought in a region in southeastern Queensland, Australia. In this approach, data describing the distribution of eight drought-contributing factors were prepared for input into the models to serve as independent variables. Relative departures of rainfall (RDR) and relative departures of soil moisture (RDSM) were analyzed to identify locations where drought conditions have occurred. The set of locations in the study area identified as having experienced drought conditions was randomly divided into two groups, 70% were used for training and 30% for validation. The models employed these data to generate maps that predict the locations that would be expected to experience drought. The prediction accuracy of the model-produced drought maps was scrutinized with two evaluation metrics: area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). The results demonstrate that the hybridized models (ANFIS-BA (AUCmean = 83.7%, RMSEmean = 0.236), ANFIS-GA (AUCmean = 81.62%, RMSEmean = 0.247), ANFIS-ICA (AUCmean = 82.12%, RMSEmean = 0.247), and ANFIS-PSO (AUCmean = 81.42%, RMSEmean = 0.255)) yield better predictive performance than the standalone ANFIS model (AUCmean = 71.8%, RMSEmean = 0.344). Furthermore, sensitivity analyses indicated that plant-available water capacity, the percentage of soil comprised of sand, and mean annual precipitation were the most important predictors of drought hazard. The versatility of the new approach for spatial drought modeling and the capacity of ANFIS model hybridization to improve model performance suggests great potential to assist decision makers in their formulations of drought risk, recovery, and response management, and in the development of contingency plans.
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Affiliation(s)
- Omid Rahmati
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
| | - Mahdi Panahi
- Division of Science Education, Kangwon National University, Chuncheon-si, Gangwon-do 24341, South Korea; Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, South Korea
| | - Zahra Kalantari
- Stockholm University, Department of Physical Geography and Bolin Centre for Climate Research, SE-106 91 Stockholm, Sweden
| | - Elinaz Soltani
- Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran
| | - Fatemeh Falah
- Department of Watershed Management, Faculty of Natural Resources and Agriculture, Lorestan University, Lorestan, Iran
| | - Kavina S Dayal
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sandy Bay 7005, Tasmania, Australia
| | - Farnoush Mohammadi
- Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran
| | - Ravinesh C Deo
- School of Agricultural, Computational and Environmental Sciences, Centre for Sustainable Agricultural Systems & Centre for Applied Climate Sciences, Institute of Life Sciences and the Environment, University of Southern Queensland, Springfield, QLD 4300, Australia
| | - John Tiefenbacher
- Department of Geography, Texas State University, San Marcos, TX 78666, USA
| | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam.
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11
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Bui DT, Khosravi K, Karimi M, Busico G, Khozani ZS, Nguyen H, Mastrocicco M, Tedesco D, Cuoco E, Kazakis N. Enhancing nitrate and strontium concentration prediction in groundwater by using new data mining algorithm. Sci Total Environ 2020; 715:136836. [PMID: 32007881 DOI: 10.1016/j.scitotenv.2020.136836] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 01/19/2020] [Accepted: 01/19/2020] [Indexed: 05/14/2023]
Abstract
Groundwater resources constitute the main source of clean fresh water for domestic use and it is essential for food production in the agricultural sector. Groundwater has a vital role for water supply in the Campanian Plain in Italy and hence a future sustainability of the resource is essential for the region. In the current paper novel data mining algorithms including Gaussian Process (GP) were used in a large groundwater quality database to predict nitrate (contaminant) and strontium (potential future increasing) concentrations in groundwater. The results were compared with M5P, random forest (RF) and random tree (RT) algorithms as a benchmark to test the robustness of the modeling process. The dataset includes 246 groundwater quality samples originating from different wells, municipals and agricultural. It was divided for the modeling process into two subgroups by using the 10-fold cross validation technique including 173 samples for model building (training dataset) and 73 samples for model validation (testing dataset). Different water quality variables including T, pH, EC, HCO3-, F-, Cl-, SO42-, Na+, K+, Mg2+, and Ca2+ have been used as an input to the models. At first stage, different input combinations have been constructed based on correlation coefficient and thus the optimal combination was chosen for the modeling phase. Different quantitative criteria alongside with visual comparison approach have been used for evaluating the modeling capability. Results revealed that to obtain reliable results also variables with low correlation should be considered as an input to the models together with those variables showing high correlation coefficients. According to the model evaluation criteria, GP algorithm outperforms all the other models in predicting both nitrate and strontium concentrations followed by RF, M5P and RT, respectively. Result also revealed that model's structure together with the accuracy and structure of the data can have a relevant impact on the model's results.
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Affiliation(s)
- Dieu Tien Bui
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
| | | | - Mahshid Karimi
- Department of Watershed Management, Sari Agricultural Science and Natural Resources University, Sari, Iran
| | - Gianluigi Busico
- University of Campania "Luigi Vanvitelli", Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, Caserta, Italy
| | - Zohreh Sheikh Khozani
- Department of Civil Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan, Malaysia
| | - Hoang Nguyen
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam.
| | - Micol Mastrocicco
- University of Campania "Luigi Vanvitelli", Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, Caserta, Italy
| | - Dario Tedesco
- University of Campania "Luigi Vanvitelli", Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, Caserta, Italy; Istituto Nazionale di Geofisica e Vulcanologia, sezione di Napoli - Osservatorio Vesuvuviano, Via Diocleziano 328 - Napoli, Italy
| | - Emilio Cuoco
- University of Campania "Luigi Vanvitelli", Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, Caserta, Italy
| | - Nerantzis Kazakis
- Aristotle University of Thessaloniki, Department of Geology, Lab. of Engineering Geology & Hydrogeology, 54124 Thessaloniki, Greece.
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12
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Vo T, Sharma R, Kumar R, Son LH, Pham BT, Tien Bui D, Priyadarshini I, Sarkar M, Le T. Crime rate detection using social media of different crime locations and Twitter part-of-speech tagger with Brown clustering. IFS 2020. [DOI: 10.3233/jifs-190870] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Thanh Vo
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
| | - Rohit Sharma
- Department of Electronics & Communication Engineering, SRM IST, NCR Campus, India
| | - Raghvendra Kumar
- Department of Computer Science and Engineering, GIET University, Gunupur, India
| | - Le Hoang Son
- VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam
| | - Binh Thai Pham
- Geotechnical Engineering and Artificial Intelligence research group (GEOAI), University of Transport Technology, Hanoi, Vietnam
| | - Dieu Tien Bui
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | | | - Manash Sarkar
- School of Computer Science& Engineering, Vellore Institute of Technology, Vellore, India
| | - Tuong Le
- Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam
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13
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Moayedi H, Mehrabi M, Bui DT, Pradhan B, Foong LK. Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility. J Environ Manage 2020; 260:109867. [PMID: 32090793 DOI: 10.1016/j.jenvman.2019.109867] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 10/23/2019] [Accepted: 11/12/2019] [Indexed: 06/10/2023]
Abstract
Forests are important dynamic systems which are widely affected by fire worldwide. Due to the complexity and non-linearity of the forest fire problem, employing hybrid evolutionary algorithms is a logical task to achieve a reliable approximation of this environmental threat. Three fuzzy-metaheuristic ensembles, based on adaptive neuro-fuzzy inference systems (ANFIS) incorporated with genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) evolutionary algorithms are used to produce the forest fire susceptibility map (FFSM) of a fire-prone region in Iran. A sensitivity analysis is also executed to evaluate the effectiveness of the proposed ensembles in terms of time and complexity. The results revealed that all models produce FFSMs with acceptable accuracy. However, the superiority of the GA-ANFIS was shown in both recognizing the pattern (AUROCtrain = 0.912 and Error = 0.1277) and predicting unseen fire events (AUROCtest = 0.850 and Error = 0.1638). The optimized structures of the proposed GA-ANFIS and PSO-ANFIS ensembles could be good alternatives to traditional forest fire predictive models, and their FFSMs can be promisingly used for future planning and decision making in the proposed area.
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Affiliation(s)
- Hossein Moayedi
- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
| | - Mohammad Mehrabi
- Department of Civil Engineering, Kermanshah University of Technology, Kermanshah, Iran
| | - Dieu Tien Bui
- Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, N-3800, Bø i Telemark, Norway
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, 2007, NSW, Australia; Department of Energy and Mineral Resources Engineering, 209 Neungdong-ro Gwangjin-gu, 05006, Seoul, South Korea
| | - Loke Kok Foong
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam.
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14
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Costache R, Tien Bui D. Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles. Sci Total Environ 2020; 712:136492. [PMID: 31927448 DOI: 10.1016/j.scitotenv.2019.136492] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 12/31/2019] [Accepted: 12/31/2019] [Indexed: 06/10/2023]
Abstract
Taking into account the exponential growth of the number of flash-floods events worldwide, the detection of areas prone to these natural hazards is one of the main activities taken in order to mitigate the negative effects of these risk phenomena. In the present paper, new modeling approaches, Alternating Decision Tree (ADT) integrated with IOE (ADT-IOE) and ADT integrated with AHP (ADT-AHP), were proposed for flash-flood susceptibility mapping across the Suha river catchment (Romania). Besides, two stand-alone methods, Index of Entropy (IOE) and Analytical Hierarchy Process (AHP), were also investigated. For this regard, 111 torrential points and 111 non-torrential points along with 8 flash-flood conditioning factors have been involved in the training process of the four models. The quality of the flash-flood models was checked by using the ROC Curve method, classification accuracy (CLA), and Kappa index. The result shows that the two ensemble models, the ADT-IOE (AUC = 0.972, CLC = 86.37%, Kappa statistics = 0.727) and the ADT-AHP (AUC = 0.926, CLA = 87.88%, Kappa statistics = 0.758), have high prediction performance and outperform the other models. Therefore, ADT-IOE and ADT-AHP are new and promising tools for flash-flood susceptibility modeling.
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Affiliation(s)
- Romulus Costache
- Research Institute of the University of Bucharest, 90-92 Sos. Panduri, 5th District, 050663 Bucharest, Romania; National Institute of Hydrology and Water Management, București-Ploiești Road, 97E, 1st District, 013686 Bucharest, Romania
| | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam; Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, N-3800 Bø i Telemark, Norway.
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15
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Saha S, Roy J, Arabameri A, Blaschke T, Tien Bui D. Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India. Sensors (Basel) 2020; 20:E1313. [PMID: 32121238 PMCID: PMC7085763 DOI: 10.3390/s20051313] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 02/25/2020] [Accepted: 02/26/2020] [Indexed: 12/02/2022]
Abstract
Gully erosion is a form of natural disaster and one of the land loss mechanisms causing severe problems worldwide. This study aims to delineate the areas with the most severe gully erosion susceptibility (GES) using the machine learning techniques Random Forest (RF), Gradient Boosted Regression Tree (GBRT), Naïve Bayes Tree (NBT), and Tree Ensemble (TE). The gully inventory map (GIM) consists of 120 gullies. Of the 120 gullies, 84 gullies (70%) were used for training and 36 gullies (30%) were used to validate the models. Fourteen gully conditioning factors (GCFs) were used for GES modeling and the relationships between the GCFs and gully erosion was assessed using the weight-of-evidence (WofE) model. The GES maps were prepared using RF, GBRT, NBT, and TE and were validated using area under the receiver operating characteristic(AUROC) curve, the seed cell area index (SCAI) and five statistical measures including precision (PPV), false discovery rate (FDR), accuracy, mean absolute error (MAE), and root mean squared error (RMSE). Nearly 7% of the basin has high to very high susceptibility for gully erosion. Validation results proved the excellent ability of these models to predict the GES. Of the analyzed models, the RF (AUROC = 0.96, PPV = 1.00, FDR = 0.00, accuracy = 0.87, MAE = 0.11, RMSE = 0.19 for validation dataset) is accurate enough for modeling and better suited for GES modeling than the other models. Therefore, the RF model can be used to model the GES areas not only in this river basin but also in other areas with the same geo-environmental conditions.
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Affiliation(s)
- Sunil Saha
- Department of Geography, University of Gour Banga, Malda, West Bengal 732103, India;
| | - Jagabandhu Roy
- Research Scholar, Dept. of Geography, University of Gour Banga, Malda, West Bengal 732103, India;
| | - Alireza Arabameri
- Department of Geomorphology, Tarbiat Modares University, Tehran 14117-13116, Iran
| | - Thomas Blaschke
- Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria;
| | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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16
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Tien Bui D, Hoang ND, Martínez-Álvarez F, Ngo PTT, Hoa PV, Pham TD, Samui P, Costache R. A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area. Sci Total Environ 2020; 701:134413. [PMID: 31706212 DOI: 10.1016/j.scitotenv.2019.134413] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Revised: 09/10/2019] [Accepted: 09/10/2019] [Indexed: 06/10/2023]
Abstract
This research proposes and evaluates a new approach for flash flood susceptibility mapping based on Deep Learning Neural Network (DLNN)) algorithm, with a case study at a high-frequency tropical storm area in the northwest mountainous region of Vietnam. Accordingly, a DLNN structure with 192 neurons in 3 hidden layers was proposed to construct an inference model that predicts different levels of susceptibility to flash flood. The Rectified Linear Unit (ReLU) and the sigmoid were selected as the activate function and the transfer function, respectively, whereas the Adaptive moment estimation (Adam) was used to update and optimize the weights of the DLNN. A database for the study area, which includes factors of elevation, slope, curvature, aspect, stream density, NDVI, soil type, lithology, and rainfall, was established to train and validate the proposed model. Feature selection was carried out for these factors using the Information gain ratio. The results show that the DLNN attains a good prediction accuracy with Classification Accuracy Rate = 92.05%, Positive Predictive Value = 94.55% and Negative Predictive Value = 89.55%. Compared to benchmarks, Multilayer Perceptron Neural Network and Support Vector Machine, the DLNN performs better; therefore, it could be concluded that the proposed hybridization of GIS and deep learning can be a promising tool to assist the government authorities and involving parties in flash flood mitigation and land-use planning.
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Affiliation(s)
- Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam
| | - Nhat-Duc Hoang
- Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, P809 - 03 Quang Trung, Da Nang 550000, Viet Nam
| | | | - Phuong-Thao Thi Ngo
- Faculty of Information Technology, Hanoi University of Mining and Geology, 14 Pho Vien, Bac Tu Liem, Hanoi, Viet Nam
| | - Pham Viet Hoa
- Ho Chi Minh City Institute of Resources Geography, Vietnam Academy of Science and Technology, Mac Dinh Chi 1, Ben Nghe, 1 District, Ho Chi Minh City 700000, Viet Nam
| | - Tien Dat Pham
- Geoinformatics Unit, the RIKEN Center for Advanced Intelligence Project (AIP), Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Pijush Samui
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
| | - Romulus Costache
- Research Institute of the University of Bucharest, 36-46 Bd. M. Kogălniceanu, 5th District, 050107 Bucharest, Romania; National Institute of Hydrology and Water Management, București-Ploiești Road, 97E, 1st 24 District, 013686, Bucharest, Romania
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17
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Rahmati O, Falah F, Dayal KS, Deo RC, Mohammadi F, Biggs T, Moghaddam DD, Naghibi SA, Bui DT. Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia. Sci Total Environ 2020; 699:134230. [PMID: 31522053 DOI: 10.1016/j.scitotenv.2019.134230] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2019] [Revised: 08/20/2019] [Accepted: 08/31/2019] [Indexed: 06/10/2023]
Abstract
A quantitative understanding of the hydro-environmental factors that influence the occurrence of agricultural drought events would enable more strategic climate change adaptation and drought management plans. Practical drought hazard mapping remains challenging due to possible exclusion of the most pertinent drought drivers, and to the use of inadequate predictive models that cannot describe drought adequately. This research aims to develop new approaches to map agricultural drought hazard with state-of-the-art machine learning models, including classification and regression trees (CART), boosted regression trees (BRT), random forests (RF), multivariate adaptive regression splines (MARS), flexible discriminant analysis (FDA) and support vector machines (SVM). Hydro-environmental datasets were used to calculate the relative departure of soil moisture (RDSM) for eight severe droughts for drought-prone southeast Queensland, Australia, over the period 1994-2013. RDSM was then used to generate an agricultural drought inventory map. Eight hydro-environmental factors were used as potential predictors of drought. The goodness-of-fit and predictive performance of all models were evaluated using different threshold-dependent and threshold-independent methods, including the true skill statistic (TSS), Efficiency (E), F-score, and the area under the receiver operating characteristic curve (AUC-ROC). The RF model (AUC-ROC = 97.7%, TSS = 0.873, E = 0.929, F-score = 0.898) yielded the highest accuracy, while the FDA model (with AUC-ROC = 73.9%, TSS = 0.424, E = 0.719, F-score = 0.512) showed the worst performance. The plant available water holding capacity (PAWC), mean annual precipitation, and clay content were the most important variables to be used for predicting the agricultural drought. About 21.2% of the area is in high or very high drought risk classes, and therefore, warrant drought and environmental protection policies. Importantly, the models do not require data on the precipitation anomaly for any given drought year; the spatial patterns in AGH were consistent for all drought events, despite very different spatial patterns in precipitation anomaly among events. Such machine-learning approaches are able to construct an overall risk map, thus assisting in the adoption of a robust drought contingency planning measure not only for this area, but also, in other regions where drought presents a pressing challenge, including its influence on key practical dimensions of social, environmental and economic sustainability.
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Affiliation(s)
- Omid Rahmati
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
| | - Fatemeh Falah
- Department of Watershed management Engineering, Lorestan University, Lorestan, Iran
| | - Kavina Shaanu Dayal
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sandy Bay, 7005, Tasmania, Australia
| | - Ravinesh C Deo
- School of Sciences, Centre for Sustainable Agricultural Systems, Centre for Applied Climate Sciences, University of Southern Queensland, Springfield, QLD 4300, Australia.
| | | | - Trent Biggs
- Department of Geography, San Diego State University, San Diego, CA 92182, USA
| | - Davoud Davoudi Moghaddam
- Department of Watershed Management, Faculty of Agriculture and Natural Resources, Lorestan University, Khorramabad, Iran
| | - Seyed Amir Naghibi
- Department of Watershed Management Engineering, Tarbiat Modares University (TMU), Tehran, Iran
| | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam.
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Arabameri A, Blaschke T, Pradhan B, Pourghasemi HR, Tiefenbacher JP, Bui DT. Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study. Sensors (Basel) 2020; 20:E335. [PMID: 31936038 PMCID: PMC7014250 DOI: 10.3390/s20020335] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 12/22/2019] [Accepted: 12/31/2019] [Indexed: 11/16/2022]
Abstract
Gully erosion is a problem; therefore, it must be predicted using highly accurate predictive models to avoid losses caused by gully development and to guarantee sustainable development. This research investigates the predictive performance of seven multiple-criteria decision-making (MCDM), statistical, and machine learning (ML)-based models and their ensembles for gully erosion susceptibility mapping (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 gully head cuts and 15 conditioning factors. The database was divided 70:30 to train and verify the models. Their performance was assessed with the area under prediction rate curve (AUPRC), the area under success rate curve (AUSRC), accuracy, and kappa. Results show that slope is key to gully formation. The maximum entropy (ME) ML model has the best performance (AUSRC = 0.947, AUPRC = 0.948, accuracy = 0.849 and kappa = 0.699). The second best is the random forest (RF) model (AUSRC = 0.965, AUPRC = 0.932, accuracy = 0.812 and kappa = 0.624). By contrast, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model was the least effective (AUSRC = 0.871, AUPRC = 0.867, accuracy = 0.758 and kappa = 0.516). RF increased the performance of statistical index (SI) and frequency ratio (FR) statistical models. Furthermore, the combination of a generalized linear model (GLM), and functional data analysis (FDA) improved their performances. The results demonstrate that a combination of geographic information systems (GIS) with remote sensing (RS)-based ML models can successfully map gully erosion susceptibility, particularly in low-income and developing regions. This method can aid the analyses and decisions of natural resources managers and local planners to reduce damages by focusing attention and resources on areas prone to the worst and most damaging gully erosion.
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Affiliation(s)
- Alireza Arabameri
- Department of Geomorphology, Tarbiat Modares University, Tehran 36581-17994, Iran;
| | - Thomas Blaschke
- Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria;
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia;
- Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
| | - Hamid Reza Pourghasemi
- Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz 71441-65186, Iran
| | | | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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19
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Mafi-Gholami D, Zenner EK, Jaafari A, Riyahi Bakhtyari HR, Tien Bui D. Multi-hazards vulnerability assessment of southern coasts of Iran. J Environ Manage 2019; 252:109628. [PMID: 31585255 DOI: 10.1016/j.jenvman.2019.109628] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 09/12/2019] [Accepted: 09/23/2019] [Indexed: 06/10/2023]
Abstract
Coastal vulnerability assessment has become one of the most important tools for decision making and providing effective managerial solutions to reduce adverse socio-economic impacts of multiple environmental hazards on coupled social-ecological systems of coastal areas. The aim of this study was to assess the vulnerability of the northern coasts of the Persian Gulf (PG) and the Gulf of Oman (GO) in the Hormozgan province of Iran. Nine variables of vulnerability that included the rate of coastline change, relative sea level rise, coastal slope, mean tidal range, coastal geomorphology, significant wave height (SWH), extreme storm surge, population density, and fishing intensity were weighted, mapped, and combined into the Coastal vulnerability index (CVI). Experts viewed sea level rise, shoreline change and extreme storm surge as most important for imparting vulnerabilities on the northern coasts of PG and GO. Socio-economic variables (i.e., population density and fishery intensity) were considered least important. Of the total length of the provincial shoreline, 27% were classified into the very low vulnerability class, 31% into the low, 17.4% into the moderate, 15.4% into the high, and 9.2% into the very high vulnerability class. About 1295 km (58%) of shorelines were classified into the low and very low vulnerability classes (CVI value ≤ 8.32) and mainly consisted of shorelines on the western coast along the PG. In contrast, 553 km (24.6%) of shorelines were classified into the high and very high vulnerability classes (CVI values > 13.39) and were located along the central coasts (especially in the Qeshm Island and Strait of Hormuz) and on the east coasts of the GO. At least a quarter of all shorelines in the province have high and very high vulnerability to environmental hazards that are the harbingers of climate change.
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Affiliation(s)
- Davood Mafi-Gholami
- Department of Forest Sciences, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, Iran.
| | - Eric K Zenner
- Department of Ecosystem Science and Management, The Pennsylvania State University, Forest Resources Building, University Park, PA, 16802, USA
| | - Abolfazl Jaafari
- Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran
| | - Hamid Reza Riyahi Bakhtyari
- Department of Forest Sciences, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, Iran
| | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam.
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20
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Costache R, Tien Bui D. Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: A case study at the Putna river catchment of Romania. Sci Total Environ 2019; 691:1098-1118. [PMID: 31466192 DOI: 10.1016/j.scitotenv.2019.07.197] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 07/13/2019] [Accepted: 07/13/2019] [Indexed: 06/10/2023]
Abstract
Flash-flood is considered to be one of the most destructive natural hazards in the world, which is difficult to accurately model and predict. The objective of the present research is to propose new ensembles of bivariate statistics and artificial intelligences and to introduce a comprehensive methodology for predicting flood susceptibility. The Putna river catchment of Romania is selected as a case study. In this regard, a total of six ensemble models were proposed and verified: Multilayer Perceptron neural network-Frequency Ratio (MLP-FR), Multilayer Perceptron neural network -Weights of Evidence (MLP-WOE), Rotation Forest-Frequency Ratio (RF-FR), Rotation Forest-Weights of Evidence (RF-WOE), Classification and Regression Tree-Frequency Ratio (CART-FR), and Classification and Regression Tree-Weights of Evidence (CART-WOE). In a first step, a geospatial database was created for the study area. This database includes 132 flood locations and 14 conditioning factors (lithology, slope angle, plan curvature, hydrological soil group, topographic wetness index, landuse, convergence index, elevation, distance from river, profile curvature, rainfall, aspect, stream power index, and topographic position index). In the next step, the Information Gain Ratio was used to evaluate the predictive ability of these factors. Subsequently, the database was used to train and validate the six ensemble models. The Receiver operating characteristic (ROC) curve, area under the curve (AUC), and statistical measures were used to evaluate the performance of the models. The results show that the prediction capability of the proposed ensemble models varied from 86.8% (the RF-FR model) to 93.9% (the RF-WOE model). These values indicate a high prediction performance for all the models. Therefore, we can state that the proposed ensemble models are new reliable tools which can be used for flood susceptibility modelling.
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Affiliation(s)
- Romulus Costache
- Research Institute of the University of Bucharest, 36-46 Bd. M. Kogalniceanu, 5th District, 050107 Bucharest, Romania; National Institute of Hydrology and Water Management, București-Ploiești Road, 97E, 1st District, 013686 Bucharest, Romania.
| | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam; Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, N-3800 Bø i Telemark, Norway.
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21
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Moayedi H, Tien Bui D, Kok Foong L. Slope Stability Monitoring Using Novel Remote Sensing Based Fuzzy Logic. Sensors (Basel) 2019; 19:s19214636. [PMID: 31653112 PMCID: PMC6864694 DOI: 10.3390/s19214636] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 10/11/2019] [Accepted: 10/22/2019] [Indexed: 11/16/2022]
Abstract
By the assist of remotely sensed data, this study examines the viability of slope stability monitoring using two novel conventional models. The proposed models are considered to be the combination of neuro-fuzzy (NF) system along with invasive weed optimization (IWO) and elephant herding optimization (EHO) evolutionary techniques. Considering the conditioning factors of land use, lithology, soil type, rainfall, distance to the road, distance to the river, slope degree, elevation, slope aspect, profile curvature, plan curvature, stream power index (SPI), and topographic wetness index (TWI), it is aimed to achieve a reliable approximation of landslide occurrence likelihood for unseen environmental conditions. To this end, after training the proposed EHO-NF and IWO-NF ensembles using training landslide events, their generalization power is evaluated by receiving operating characteristic curves. The results demonstrated around 75% accuracy of prediction for both models; however, the IWO-NF achieved a better understanding of landslide distribution pattern. Due to the successful performance of the implemented models, they could be promising alternatives to mathematical and analytical approaches being used for discerning the relationship between the slope failure and environmental parameters.
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Affiliation(s)
- Hossein Moayedi
- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
- Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
| | - Dieu Tien Bui
- Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, N-3800 Bø i Telemark, Norway.
| | - Loke Kok Foong
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.
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22
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Arabameri A, Yamani M, Pradhan B, Melesse A, Shirani K, Tien Bui D. Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility. Sci Total Environ 2019; 688:903-916. [PMID: 31255826 DOI: 10.1016/j.scitotenv.2019.06.205] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 06/12/2019] [Accepted: 06/13/2019] [Indexed: 06/09/2023]
Abstract
Gully erosion is considered as a severe environmental problem in many areas of the world which causes huge damages to agricultural lands and infrastructures (i.e. roads, buildings, and bridges); however, gully erosion modeling and prediction with high accuracy are still difficult due to the complex interactions of various factors. The objective of this research was to develop and introduce three new ensemble models, which were based on Complex Proportional Assessment of Alternatives (COPRAS), Logistic Regression (LR), Boosted Regression Tree (BRT), Random Forest (RF), and Frequency Ratio (FR) for spatial prediction of gully erosion with a case study at the Najafabad watershed (Iran). For this purpose, a total of 290 head-cut of gullies and 17 conditioning factors were collected and used to establish a geospatial database. Subsequently, FR was used to determine the spatial relationship between the conditioning factors and the head-cut of gullies, whereas RF, BRT, and LR were used to quantify the relative importance of these factors. In the next step, three ensemble gully erosion models, named COPRAS-FR-RF, COPRAS-FR-BRT, and COPRAS-FR-LR were developed and verified. The Success Rate Curve (SRC), and the Prediction Rate Curve (PRC) and their areas under the curves (AUC) were used to check the performance of the three proposed models. The result showed that Soil group, geomorphology, and drainage density factors played the key role on the occurrence of the gully erosion. All the three models have very high degree-of-fit and the prediction performance, the COPRAS-FR-RF model (AUC-SRC = 0.974 and AUC-PRC = 0.929), the COPRAS-FR-BRT model (AUC-SRC = 0.973 and AUC-PRC = 0.928), and the COPRAS-FR-LR model (AUC-SRC = 0.972 and AUC-PRC = 0.926); therefore, it is concluded that they are efficient and new powerful tools which could be used for predicting gully erosion in prone-areas.
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Affiliation(s)
- Alireza Arabameri
- Department of Geomorphology, Tarbiat Modares University, Tehran, Iran.
| | - Mojtaba Yamani
- Department of Geomorphology, Tehran University, Tehran, Iran
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, 2007, New South Wales, Australia; Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea
| | - Assefa Melesse
- Department of Earth and Environment, Florida International University, USA
| | - Kourosh Shirani
- Soil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran
| | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam.
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23
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Rahmati O, Choubin B, Fathabadi A, Coulon F, Soltani E, Shahabi H, Mollaefar E, Tiefenbacher J, Cipullo S, Ahmad BB, Tien Bui D. Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods. Sci Total Environ 2019; 688:855-866. [PMID: 31255823 DOI: 10.1016/j.scitotenv.2019.06.320] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Revised: 06/19/2019] [Accepted: 06/20/2019] [Indexed: 06/09/2023]
Abstract
Although estimating the uncertainty of models used for modelling nitrate contamination of groundwater is essential in groundwater management, it has been generally ignored. This issue motivates this research to explore the predictive uncertainty of machine-learning (ML) models in this field of study using two different residuals uncertainty methods: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Prediction-interval coverage probability (PICP), the most important of the statistical measures of uncertainty, was used to evaluate uncertainty. Additionally, three state-of-the-art ML models including support vector machine (SVM), random forest (RF), and k-nearest neighbor (kNN) were selected to spatially model groundwater nitrate concentrations. The models were calibrated with nitrate concentrations from 80 wells (70% of the data) and then validated with nitrate concentrations from 34 wells (30% of the data). Both uncertainty and predictive performance criteria should be considered when comparing and selecting the best model. Results highlight that the kNN model is the best model because not only did it have the lowest uncertainty based on the PICP statistic in both the QR (0.94) and the UNEEC (in all clusters, 0.85-0.91) methods, but it also had predictive performance statistics (RMSE = 10.63, R2 = 0.71) that were relatively similar to RF (RMSE = 10.41, R2 = 0.72) and higher than SVM (RMSE = 13.28, R2 = 0.58). Determining the uncertainty of ML models used for spatially modelling groundwater-nitrate pollution enables managers to achieve better risk-based decision making and consequently increases the reliability and credibility of groundwater-nitrate predictions.
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Affiliation(s)
- Omid Rahmati
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
| | - Bahram Choubin
- Faculty of Natural Resources, University of Tehran, Karaj, Iran
| | - Abolhasan Fathabadi
- Department of Range and Watershed Management, Gonbad Kavous University, Gonbad Kavous, Golestan Province, Iran
| | - Frederic Coulon
- Cranfield University, School of Water, Energy and Environment, Cranfield MK43 0AL, UK
| | - Elinaz Soltani
- Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran
| | - Himan Shahabi
- Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran
| | - Eisa Mollaefar
- Department of Natural Resources and Watershed Management of Golestan Province, Iran
| | - John Tiefenbacher
- Department of Geography, Texas State University, San Marcos, TX 78666, USA
| | - Sabrina Cipullo
- Cranfield University, School of Water, Energy and Environment, Cranfield MK43 0AL, UK
| | - Baharin Bin Ahmad
- Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Malaysia
| | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam.
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24
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Pham BT, Nguyen MD, Dao DV, Prakash I, Ly HB, Le TT, Ho LS, Nguyen KT, Ngo TQ, Hoang V, Son LH, Ngo HTT, Tran HT, Do NM, Van Le H, Ho HL, Tien Bui D. Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis. Sci Total Environ 2019; 679:172-184. [PMID: 31082591 DOI: 10.1016/j.scitotenv.2019.05.061] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 04/29/2019] [Accepted: 05/05/2019] [Indexed: 06/09/2023]
Abstract
In this study, we developed Different Artificial Intelligence (AI) models namely Artificial Neural Network (ANN), Adaptive Network based Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) for the prediction of Compression Coefficient of soil (Cc) which is one of the most important geotechnical parameters. A Monte Carlo approach was used for the sensitivity analysis of the AI models and input parameters. For the construction and validation of the models, 189 soft clayey soil samples were analyzed. In the models study, 13 input parameters: depth of sample, bulk density, plasticity index, moisture content, clay content, specific gravity, void ratio, liquid limit, dry density, porosity, plastic limit, degree of saturation, and liquidity index were used to obtain one output parameter "Cc". Validation of the models was done using statistical methods such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of determination (R2). Results of the model validation indicate that though performance of all the three models is good but SVM model is the best in the prediction of Cc. The Monte Carlo method based sensitivity analysis results show that out of the 13 input parameters considered for the models study, four parameters namely clay, degree of saturation, specific gravity and depth of sample are the most relevant in the prediction of Cc, and other parameters (bulk density, dry density, void ratio and porosity) are the most insignificant parameters for the prediction of Cc. Removal of these insignificant parameters helped to reduce the dimension of the input space and also model running time, and improved significantly the performance of the AI models. The results of this study might help in selecting the suitable AI models and input parameters for better and quick prediction of the Cc of soil.
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Affiliation(s)
- Binh Thai Pham
- University of Transport Technology, Hanoi 100000, Viet Nam.
| | - Manh Duc Nguyen
- Department of Geotechnical Engineering, University of Transport and Communications, Hanoi 100000, Vietnam
| | - Dong Van Dao
- University of Transport Technology, Hanoi 100000, Viet Nam.
| | - Indra Prakash
- Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382007, India
| | - Hai-Bang Ly
- University of Transport Technology, Hanoi 100000, Viet Nam.
| | - Tien-Thinh Le
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam.
| | - Lanh Si Ho
- University of Transport Technology, Hanoi 100000, Viet Nam
| | | | - Trinh Quoc Ngo
- University of Transport Technology, Hanoi 100000, Viet Nam
| | - Vu Hoang
- University of Transport Technology, Hanoi 100000, Viet Nam
| | - Le Hoang Son
- VNU Information Technology Institute, Vietnam National University, Hanoi, Viet Nam
| | | | | | - Ngoc Minh Do
- University of Transport Technology, Hanoi 100000, Viet Nam
| | - Hiep Van Le
- University of Transport Technology, Hanoi 100000, Viet Nam
| | - Huu Loc Ho
- NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, Viet Nam.
| | - Dieu Tien Bui
- Geographic Information System Group, Department of Business and IT, University College of Southeast Norway, Bø i Telemark N-3800, Norway.
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25
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Bui DT, Moayedi H, Kalantar B, Osouli A, Pradhan B, Nguyen H, Rashid ASA. A Novel Swarm Intelligence-Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility. Sensors (Basel) 2019; 19:E3590. [PMID: 31426552 PMCID: PMC6719036 DOI: 10.3390/s19163590] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 08/07/2019] [Accepted: 08/15/2019] [Indexed: 11/27/2022]
Abstract
In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors-elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall-is prepared to develop the ANN and HHO-ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO-ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO-ANN = 0.773) the landslide pattern.
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Affiliation(s)
- Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
| | - Hossein Moayedi
- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
- Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
| | - Bahareh Kalantar
- RIKEN Center for Advanced Intelligence Project, Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 103-0027, Japan
| | - Abdolreza Osouli
- Civil Engineering Department, Southern Illinois University, Edwardsville, IL 62026, USA
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia
- Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
| | - Hoang Nguyen
- Department of Surface Mining, Hanoi University of Mining land Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam
- Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam
| | - Ahmad Safuan A Rashid
- Centre of Tropical Geoengineering (Geotropik), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
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26
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Jaafari A, Razavi Termeh SV, Bui DT. Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability. J Environ Manage 2019; 243:358-369. [PMID: 31103681 DOI: 10.1016/j.jenvman.2019.04.117] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 04/27/2019] [Accepted: 04/28/2019] [Indexed: 06/09/2023]
Abstract
In the terrestrial ecosystems, perennial challenges of increased frequency and intensity of wildfires are exacerbated by climate change and unplanned human activities. Development of robust management and suppression plans requires accurate estimates of future burn probabilities. This study describes the development and validation of two hybrid intelligence predictive models that rely on an adaptive neuro-fuzzy inference system (ANFIS) and two metaheuristic optimization algorithms, i.e., genetic algorithm (GA) and firefly algorithm (FA), for the spatially explicit prediction of wildfire probabilities. A suite of ten explanatory variables (altitude, slope, aspect, land use, rainfall, soil order, temperature, wind effect, and distance to roads and human settlements) was investigated and a spatial database constructed using 32 fire events from the Zagros ecoregion (Iran). The frequency ratio model was used to assign weights to each class of variables that depended on the strength of the spatial association between each class and the probability of wildfire occurrence. The weights were then used for training the ANFIS-GA and ANFIS-FA hybrid models. The models were validated using the ROC-AUC method that indicated that the ANFIS-GA model performed better (AUCsuccessrate = 0.92; AUCpredictionrate = 0.91) than the ANFIS-FA model (AUCsuccessrate = 0.89; AUCpredictionrate = 0.88). The efficiency of these models was compared to a single ANFIS model and statistical analyses of paired comparisons revealed that the two meta-optimized predictive models significantly improved wildfire prediction accuracy compared to the single ANFIS model (AUCsuccessrate = 0.82; AUCpredictionrate = 0.78). We concluded that such predictive models may become valuable toolkits to effectively guide fire management plans and on-the-ground decisions on firefighting strategies.
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Affiliation(s)
- Abolfazl Jaafari
- Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran, Iran
| | | | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam.
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27
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Rahmati O, Falah F, Naghibi SA, Biggs T, Soltani M, Deo RC, Cerdà A, Mohammadi F, Tien Bui D. Land subsidence modelling using tree-based machine learning algorithms. Sci Total Environ 2019; 672:239-252. [PMID: 30959291 DOI: 10.1016/j.scitotenv.2019.03.496] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 03/21/2019] [Accepted: 03/31/2019] [Indexed: 06/09/2023]
Abstract
Land subsidence (LS) is among the most critical environmental problems, affecting both agricultural sustainability and urban infrastructure. Existing methods often use either simple regression models or complex hydraulic models to explain and predict LS. There are few studies that identify the risk factors and predict the risk of LS using machine learning models. This study compares four tree-based machine learning models for land subsidence hazard modelling at a study area in Hamadan plain (Iran). The study also analyzes the importance of six risk factors including topography (elevation, slope), geomorphology (distance from stream, drainage density), hydrology (groundwater drawdown) and lithology on LS. Thematic layers of each variable related to the LS phenomenon are prepared and utilized as the inputs to the four tree-based machine learning models, including the Rule-Based Decision Tree (RBDT), Boosted Regression Trees (BRT), Classification And Regression Tree (CART), and the Random Forest (RF) algorithms to produce a consolidated LS hazard map. The accuracy of the generated maps is then evaluated using the area under the receiver operating characteristic curve (AUC) and the True Skill Statistics (TSS). The RF approach had the lowest predictive error for mapping the LS hazard (i.e., AUC 96.7% for training, AUC 93.8% for validation, TSS 0.912 for training, TSS 0.904 for validation) followed by BRT. Groundwater drawdown was seen to be the most influential factor that contributed to land subsidence in the present study area, followed by lithology and distance from the stream network.
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Affiliation(s)
- Omid Rahmati
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
| | - Fatemeh Falah
- Young Researchers and Elites Club, Khorramabad Branch, Islamic Azad University, Khorramabad, Iran
| | - Seyed Amir Naghibi
- Department of Watershed Management Engineering, Tarbiat Modares University, Mazandaran, Iran
| | - Trent Biggs
- Department of Geography, San Diego State University, San Diego, CA 92182, USA
| | - Milad Soltani
- Department of Watershed Management, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
| | - Ravinesh C Deo
- School of Agricultural, Computational and Environmental Sciences, Centre for Sustainable Agricultural Systems & Centre for Applied Climate Sciences, Institute of Life Sciences and the Environment, University of Southern Queensland, Springfield, QLD 4300, Australia
| | - Artemi Cerdà
- Soil Erosion and Degradation Research Group, Department of Geography, Valencia University, Blasco Ibàñez, 28, 46010, Valencia, Spain
| | - Farnoush Mohammadi
- Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran
| | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam.
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28
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Bui DT, Tsangaratos P, Ngo PTT, Pham TD, Pham BT. Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Sci Total Environ 2019; 668:1038-1054. [PMID: 31018446 DOI: 10.1016/j.scitotenv.2019.02.422] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 02/26/2019] [Accepted: 02/27/2019] [Indexed: 06/09/2023]
Abstract
The main objective of the present study was to provide a novel methodological approach for flash flood susceptibility modeling based on a feature selection method (FSM) and tree based ensemble methods. The FSM, used a fuzzy rule based algorithm FURIA, as attribute evaluator, whereas GA were used as the search method, in order to obtain optimal set of variables used in flood susceptibility modeling assessments. The novel FURIA-GA was combined with LogitBoost, Bagging and AdaBoost ensemble algorithms. The performance of the developed methodology was evaluated at the Bao Yen district and the Bac Ha district of Lao Cai Province in the Northeast region of Vietnam. For the case study, 654 floods and twelve geo-environmental variables were used. The predictive performance of each model was estimated through the calculation of the classification accuracy, the sensitivity, the specificity, the success and predictive rate curve and the area under the curves (AUC). The FURIA-GA FSM compared to a conventional rule based method gave more accurate predictive results. Also, the FURIA-GA based models, presented higher learning and predictive ability compared to the ensemble models that had not undergone a FSM. Based on the predictive classification accuracy, FURIA-GA-Bagging (93.37%) outperformed FURIA-GA-LogitBoost (92.35%) and FURIA-GA-AdaBoost (89.03%). FURIA-GA-Bagging showed also the highest sensitivity (96.94%) and specificity (89.80%). On the other hand, the FURIA-GA-LogitBoost showed the lowest percentage in very high susceptible zone and the highest relative flash-flood density, whereas the FURIA-GA-AdaBoost achieved the highest prediction AUC value (0.9740), based on the prediction rate curve, followed by FURIA-GA-Bagging (0.9566), and FURIA-GA-LogitBoost (0.8955). It can be concluded that the usage of different statistical metrics, provides different outcomes concerning the best prediction model, which mainly could be attributed to sites specific settings. The proposed models could be considered as a novel alternative investigation tools appropriate for flash flood susceptibility mapping.
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Affiliation(s)
- Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam.
| | - Paraskevas Tsangaratos
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
| | - Phuong-Thao Thi Ngo
- Department of Geoinformatics, Faculty of Information Technology, Hanoi University of Mining and Geology, 18 Pho Vien, Duc Thang, Bac Tu Liem, Hanoi, Viet Nam.
| | - Tien Dat Pham
- Geoinformatics Unit, the RIKEN Center for Advanced Intelligence Project (AIP), Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan.
| | - Binh Thai Pham
- Geotechnical Engineering and Artificial Intelligence Research Group (GEOAI), University of Transport Technology, Hano, Viet Nam.
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Tien Bui D, Shirzadi A, Shahabi H, Chapi K, Omidavr E, Pham BT, Talebpour Asl D, Khaledian H, Pradhan B, Panahi M, Bin Ahmad B, Rahmani H, Gróf G, Lee S. A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran). Sensors (Basel) 2019; 19:E2444. [PMID: 31146336 PMCID: PMC6603737 DOI: 10.3390/s19112444] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 05/12/2019] [Accepted: 05/18/2019] [Indexed: 11/22/2022]
Abstract
In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).
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Affiliation(s)
- Dieu Tien Bui
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
- Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
| | - Ataollah Shirzadi
- Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran.
| | - Himan Shahabi
- Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran.
| | - Kamran Chapi
- Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran.
| | - Ebrahim Omidavr
- Department of Rangeland and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan 87317-53153, Iran.
| | - Binh Thai Pham
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.
| | - Dawood Talebpour Asl
- Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran.
| | - Hossein Khaledian
- Kurdistan Agriculture and Natural Resources Research and Education Center, AREEO, Sanandaj 66169-36311, Iran.
| | - Biswajeet Pradhan
- Center for Advanced Modeling and Geospatial System (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, CB11.06.106, Building 11, 81 Broadway, Ultimo NSW 2007, Australia.
- Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea.
| | - Mahdi Panahi
- Department of Geophysics, Young Researchers and Elites Club, North Tehran Branch, Islamic Azad University, Tehran P.O. Box 19585/466, Iran.
| | - Baharin Bin Ahmad
- Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia.
| | - Hosein Rahmani
- Department of Computer Science and Engineering, and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz 84334-71964, Iran.
| | - Gyula Gróf
- Department of Energy Engineering, Budapest University of Technology and Economics, Budapest 1111, Hungary.
| | - Saro Lee
- Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahak-ro Yuseong-gu, Daejeon 34132, Korea.
- Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Korea.
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Rahmati O, Kornejady A, Samadi M, Deo RC, Conoscenti C, Lombardo L, Dayal K, Taghizadeh-Mehrjardi R, Pourghasemi HR, Kumar S, Bui DT. PMT: New analytical framework for automated evaluation of geo-environmental modelling approaches. Sci Total Environ 2019; 664:296-311. [PMID: 30743123 DOI: 10.1016/j.scitotenv.2019.02.017] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 01/31/2019] [Accepted: 02/01/2019] [Indexed: 06/09/2023]
Abstract
Geospatial computation, data transformation to a relevant statistical software, and step-wise quantitative performance assessment can be cumbersome, especially when considering that the entire modelling procedure is repeatedly interrupted by several input/output steps, and the self-consistency and self-adaptive response to the modelled data and the features therein are lost while handling the data from different kinds of working environments. To date, an automated and a comprehensive validation system, which includes both the cutoff-dependent and -independent evaluation criteria for spatial modelling approaches, has not yet been developed for GIS based methodologies. This study, for the first time, aims to fill this gap by designing and evaluating a user-friendly model validation approach, denoted as Performance Measure Tool (PMT), and developed using freely available Python programming platform. The considered cutoff-dependent criteria include receiver operating characteristic (ROC) curve, success-rate curve (SRC) and prediction-rate curve (PRC), whereas cutoff-independent consist of twenty-one performance metrics such as efficiency, misclassification rate, false omission rate, F-score, threat score, odds ratio, etc. To test the robustness of the developed tool, we applied it to a wide variety of geo-environmental modelling approaches, especially in different countries, data, and spatial contexts around the world including, the USA (soil digital modelling), Australia (drought risk evaluation), Vietnam (landslide studies), Iran (flood studies), and Italy (gully erosion studies). The newly proposed PMT is demonstrated to be capable of analyzing a wide range of environmental modelling results, and provides inclusive performance evaluation metrics in a relatively short time and user-convenient framework whilst each of the metrics is used to address a particular aspect of the predictive model. Drawing on the inferences, a scenario-based protocol for model performance evaluation is suggested.
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Affiliation(s)
- Omid Rahmati
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
| | - Aiding Kornejady
- Young Researchers and Elite Club, Gorgan Branch, Islamic Azad University, Gorgan, Iran
| | - Mahmood Samadi
- Faculty of Natural Resources, University of Tehran, Karaj, Iran
| | - Ravinesh C Deo
- School of Agricultural, Computational and Environmental Sciences, Centre for Sustainable Agricultural Systems & Centre for Applied Climate Sciences, University of Southern Queensland, Springfield, QLD 4300, Australia
| | - Christian Conoscenti
- Department of Earth and Marine Sciences (DISTEM), University of Palermo, Via Archirafi 22, 90123 Palermo, Italy
| | - Luigi Lombardo
- Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, Netherlands
| | - Kavina Dayal
- CSIRO Agriculture and Food, 15 College Road, Sandy Bay, TAS 7005, Australia
| | - Ruhollah Taghizadeh-Mehrjardi
- Department of Geosciences, Soil Science and Geomorphology, University of Tübingen, Tübingen, Germany; Faculty of Agriculture and Natural Resources, Ardakan University, Ardakan, Iran
| | - Hamid Reza Pourghasemi
- College of Marine Sciences and Engineering, Nanjing Normal University, Nanjing, 210023, China; Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran
| | - Sandeep Kumar
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, USA
| | - Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam; Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, N-3800 Bø i Telemark, Norway.
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Tien Bui D, Hoang ND, Samui P. Spatial pattern analysis and prediction of forest fire using new machine learning approach of Multivariate Adaptive Regression Splines and Differential Flower Pollination optimization: A case study at Lao Cai province (Viet Nam). J Environ Manage 2019; 237:476-487. [PMID: 30825780 DOI: 10.1016/j.jenvman.2019.01.108] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2018] [Revised: 01/21/2019] [Accepted: 01/27/2019] [Indexed: 06/09/2023]
Abstract
Understanding spatial patterns of forest fire is of key important for fire danger management and ecological implication. This aim of this study was to propose a new machine learning methodology for analyzing and predicting spatial patterns of forest fire danger with a case study of tropical forest fire at Lao Cai province (Vietnam). For this purpose, a Geographical Information System (GIS) database for the study area was established, including ten influencing factors (slope, aspect, elevation, land use, distance to road, normalized difference vegetation index, rainfall, temperature, wind speed, and humidity) and 257 fire locations. The relevance level of these factors with the forest fire was analyzed and assessed using the Mutual Information algorithm. Then, a new hybrid artificial intelligence model named as MARS-DFP, which was Multivariate Adaptive Regression Splines (MARS) optimized by Differential Flower Pollination (DFP), was proposed and used construct forest fire model for generating spatial patterns of forest fire. MARS is employed to build the forest fire model for generalizing a classification boundary that distinguishes fire and non-fire areas, whereas DFP, a metaheuristic approach, was utilized to optimize the model. Finally, global prediction performance of the model was assessed using Area Under the curve (AUC), Classification Accuracy Rate (CAR), Wilcoxon signed-rank test, and various statistical indices. The result demonstrated that the predictive performance of the MARS-DFP model was high (AUC = 0.91 and CAR = 86.57%) and better to those of other benchmark methods, Backpropagation Artificial Neural Network, Adaptive neuro fuzzy inference system, Radial Basis Function Neural Network. This fact confirms that the newly constructed MARS-DFP model is a promising alternative for spatial prediction of forest fire susceptibility.
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Affiliation(s)
- Dieu Tien Bui
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam; Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, N-3800, Bø i Telemark, Norway.
| | - Nhat-Duc Hoang
- Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam
| | - Pijush Samui
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
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Pham TD, Xia J, Ha NT, Bui DT, Le NN, Tekeuchi W. A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010⁻2018. Sensors (Basel) 2019; 19:s19081933. [PMID: 31022958 PMCID: PMC6515341 DOI: 10.3390/s19081933] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 04/08/2019] [Accepted: 04/12/2019] [Indexed: 11/16/2022]
Abstract
Blue carbon (BC) ecosystems are an important coastal resource, as they provide a range of goods and services to the environment. They play a vital role in the global carbon cycle by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, there has been a large reduction in the global BC ecosystems due to their conversion to agriculture and aquaculture, overexploitation, and removal for human settlements. Effectively monitoring BC ecosystems at large scales remains a challenge owing to practical difficulties in monitoring and the time-consuming field measurement approaches used. As a result, sensible policies and actions for the sustainability and conservation of BC ecosystems can be hard to implement. In this context, remote sensing provides a useful tool for mapping and monitoring BC ecosystems faster and at larger scales. Numerous studies have been carried out on various sensors based on optical imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), aerial photographs (APs), and multispectral data. Remote sensing-based approaches have been proven effective for mapping and monitoring BC ecosystems by a large number of studies. However, to the best of our knowledge, this is the first comprehensive review on the applications of remote sensing techniques for mapping and monitoring BC ecosystems. The main goal of this review is to provide an overview and summary of the key studies undertaken from 2010 onwards on remote sensing applications for mapping and monitoring BC ecosystems. Our review showed that optical imagery, such as multispectral and hyper-spectral data, is the most common for mapping BC ecosystems, while the Landsat time-series are the most widely-used data for monitoring their changes on larger scales. We investigate the limitations of current studies and suggest several key aspects for future applications of remote sensing combined with state-of-the-art machine learning techniques for mapping coastal vegetation and monitoring their extents and changes.
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Affiliation(s)
- Tien Dat Pham
- Geoinformatics Unit, the RIKEN Center for Advanced Intelligence Project (AIP), Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan.
| | - Junshi Xia
- Geoinformatics Unit, the RIKEN Center for Advanced Intelligence Project (AIP), Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan.
| | - Nam Thang Ha
- Environmental Research Institute, School of Science, The University of Waikato, Hamilton 3240, New Zealand.
- Faculty of Fisheries, Hue University of Agriculture and Forestry, Hue 49000, Vietnam.
| | - Dieu Tien Bui
- Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, Gullbringvegen 36, N-3800 BøiTelemark, Norway.
| | - Nga Nhu Le
- Department of Marine Mechanics and Environment, Institute of Mechanics, Vietnam Academy of Science and Technology (VAST), 264 Doi Can Street, Hanoi 100000, Vietnam.
| | - Wataru Tekeuchi
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.
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Dou J, Yunus AP, Tien Bui D, Merghadi A, Sahana M, Zhu Z, Chen CW, Khosravi K, Yang Y, Pham BT. Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci Total Environ 2019; 662:332-346. [PMID: 30690368 DOI: 10.1016/j.scitotenv.2019.01.221] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 01/06/2019] [Accepted: 01/20/2019] [Indexed: 06/09/2023]
Abstract
Landslides represent a part of the cascade of geological hazards in a wide range of geo-environments. In this study, we aim to investigate and compare the performance of two state-of-the-art machine learning models, i.e., decision tree (DT) and random forest (RF) approaches to model the massive rainfall-triggered landslide occurrences in the Izu-Oshima Volcanic Island, Japan at a regional scale. At first, a landslide inventory map is prepared consisting of 44 landslide polygons (10,444 pixels) from aerial photo-interpretation and field surveys. To estimate the robustness of the models, we randomly adapted two different samples (S1 and S2), comprising of both positive and negative cells (70% of total landslides - 7293 pixels) for training and remaining (30%-3151 pixels) for validation. Twelve causative factors including altitude, slope angle, slope aspect, plan curvature, total curvature, compound topographic index, stream power index, distance to drainage network, drainage density, distance to geological boundaries, lithology and cumulative rainfall were selected as predictors to implement the landslide susceptibility model. The area under the receiver operating characteristics (ROC) curves (AUC) and other statistical signifiers were used to verify the model accuracies. The result shows that the DT and RF models achieved remarkable predictive performance (AUC > 0.9), producing near accurate susceptibility maps. The overall efficiency of RF (AUC = 0.956) is found significantly higher than the DT (AUC = 0.928) results. Additionally, we noticed that the performance of RF for modeling landslide susceptibility is very robust even though the training and validation samples are altered. Considering the performances, we suggest that both RF and DT models can be used in other similar non-eruption-related landslide studies in the tephra-deposited rich volcanoes, as they are capable of rapidly generating accurate and stable LSM maps for risk mitigation, management practices, and decision-making. Moreover, the RF-based model is promising and enough to be recommended as a method to map regional landslide susceptibility.
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Affiliation(s)
- Jie Dou
- Key Laboratory of Geological Hazards on Three Gorges Reservoir Area, Ministry of Education, China Three Gorges University, China.
| | - Ali P Yunus
- State Key Laboratory of Geo-hazard Prevention and Geo-environment Protection, Chengdu University of Technology, Chengdu, China
| | - Dieu Tien Bui
- GIS group, Department of Business and IT, University of South-Eastern Norway, Gullbringvegen 36, 3800 Bø i Telemark, Norway
| | - Abdelaziz Merghadi
- Research Laboratory of Sedimentary Environment, Mineral and Water Resources of Eastern Algeria, University of Tebessa, Tebessa 12002, Algeria
| | - Mehebub Sahana
- Department of Geography, Faculty of Natural Science, Jamia Millia Islamia, New Delhi 110025, India
| | - Zhongfan Zhu
- College of Water Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, China.
| | - Chi-Wen Chen
- National Science and Technology Center for Disaster Reduction, No. 200, Sec. 3, Beixin Road, Xindian District, New Taipei City, Taiwan
| | - Khabat Khosravi
- Department of Watershed Management, Sari Agricultural Science and Natural Resources University, Sari, Iran
| | - Yong Yang
- Institute of Industrial Science, The University of Tokyo, Japan
| | - Binh Thai Pham
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam.
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Khosravi K, Sartaj M, Tsai FTC, Singh VP, Kazakis N, Melesse AM, Prakash I, Tien Bui D, Pham BT. A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment. Sci Total Environ 2018; 642:1032-1049. [PMID: 30045486 DOI: 10.1016/j.scitotenv.2018.06.130] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Revised: 06/10/2018] [Accepted: 06/11/2018] [Indexed: 05/23/2023]
Abstract
Groundwater vulnerability assessment is a measure of potential groundwater contamination for areas of interest. The main objective of this study is to modify original DRASTIC model using four objective methods, Weights-of-Evidence (WOE), Shannon Entropy (SE), Logistic Model Tree (LMT), and Bootstrap Aggregating (BA) to create a map of groundwater vulnerability for the Sari-Behshahr plain, Iran. The study also investigated impact of addition of eight additional factors (distance to fault, fault density, distance to river, river density, land-use, soil order, geological time scale, and altitude) to improve groundwater vulnerability assessment. A total of 109 nitrate concentration data points were used for modeling and validation purposes. The efficacy of the four methods was evaluated quantitatively using the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). AUC value for original DRASTIC model without any modification of weights and rates was 0.50. Modification of weights and rates resulted in better performance with AUC values of 0.64, 0.65, 0.75, and 0.81 for BA, SE, LMT, and WOE methods, respectively. This indicates that performance of WOE is the best in assessing groundwater vulnerability for DRASTIC model with 7 factors. The results also show more improvement in predictability of the WOE model by introducing 8 additional factors to the DRASTIC as AUC value increased to 0.91. The most effective contributing factor for ground water vulnerability in the study area is the net recharge. The least effective factors are the impact of vadose zone and hydraulic conductivity.
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Affiliation(s)
- Khabat Khosravi
- Faculty of Natural Resources, Sari Agricultural Science and Natural Resources University, Sari, Iran
| | - Majid Sartaj
- Civil Engineering Department, University of Ottawa, Ottawa, Ontario K1N6N5, Canada
| | - Frank T-C Tsai
- Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Vijay P Singh
- Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A & M University, USA
| | | | - Assefa M Melesse
- Department of Earth and Environment, AHC-5-390, Florida International University, USA
| | - Indra Prakash
- Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Gandhinagar, India
| | - Dieu Tien Bui
- Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, Gullbringvegen 36, N-3800 Bø i Telemark, Norway
| | - Binh Thai Pham
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
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Shirzadi A, Soliamani K, Habibnejhad M, Kavian A, Chapi K, Shahabi H, Chen W, Khosravi K, Thai Pham B, Pradhan B, Ahmad A, Bin Ahmad B, Tien Bui D. Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping. Sensors (Basel) 2018; 18:E3777. [PMID: 30400627 PMCID: PMC6263474 DOI: 10.3390/s18113777] [Citation(s) in RCA: 112] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 09/25/2018] [Accepted: 10/16/2018] [Indexed: 11/29/2022]
Abstract
The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.
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Affiliation(s)
- Ataollah Shirzadi
- Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari P.O. Box 48181-68984, Iran.
| | - Karim Soliamani
- Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari P.O. Box 48181-68984, Iran.
| | - Mahmood Habibnejhad
- Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari P.O. Box 48181-68984, Iran.
| | - Ataollah Kavian
- Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari P.O. Box 48181-68984, Iran.
| | - Kamran Chapi
- Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran.
| | - Himan Shahabi
- Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran.
| | - Wei Chen
- College of Geology & Environment, Xi'an University of Science and Technology, Xi'an 710054, China.
| | - Khabat Khosravi
- Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari P.O. Box 48181-68984, Iran.
| | - Binh Thai Pham
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia.
- Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea.
| | - Anuar Ahmad
- Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia.
| | - Baharin Bin Ahmad
- Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia.
| | - Dieu Tien Bui
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
- Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
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Bui DT, Panahi M, Shahabi H, Singh VP, Shirzadi A, Chapi K, Khosravi K, Chen W, Panahi S, Li S, Ahmad BB. Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods. Sci Rep 2018; 8:15364. [PMID: 30337603 PMCID: PMC6193992 DOI: 10.1038/s41598-018-33755-7] [Citation(s) in RCA: 99] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 10/06/2018] [Indexed: 11/09/2022] Open
Abstract
Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas.
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Affiliation(s)
- Dieu Tien Bui
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Mahdi Panahi
- Geohazard Department Manager, Samaneh Kansar Zamin (SKZ) Company, Tehran, Iran
| | - Himan Shahabi
- Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran.
| | - Vijay P Singh
- Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A & M University, College Station, TX, 77843-2117, USA
| | - Ataollah Shirzadi
- Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran
| | - Kamran Chapi
- Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran
| | - Khabat Khosravi
- Department of Watershed Management, Faculty of Natural Resources, Sari Agricultural Science and Natural Resources University, Sari, Iran
| | - Wei Chen
- College of Geology & Environment, Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, China
| | - Somayeh Panahi
- Young Researchers and Elites Club, North Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Shaojun Li
- State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, Hubei, 430071, China
| | - Baharin Bin Ahmad
- Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia
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Tien Bui D, Shahabi H, Shirzadi A, Chapi K, Pradhan B, Chen W, Khosravi K, Panahi M, Bin Ahmad B, Saro L. Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms. Sensors (Basel) 2018; 18:E2464. [PMID: 30065216 PMCID: PMC6111310 DOI: 10.3390/s18082464] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 07/24/2018] [Accepted: 07/27/2018] [Indexed: 11/16/2022]
Abstract
In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results.
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Affiliation(s)
- Dieu Tien Bui
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
- Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
| | - Himan Shahabi
- Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran.
| | - Ataollah Shirzadi
- Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran.
| | - Kamran Chapi
- Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran.
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia.
- Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea.
| | - Wei Chen
- College of Geology & Environment, Xi'an University of Science and Technology, Xi'an 710054, China.
| | - Khabat Khosravi
- Department of Watershed Sciences Engineering, Faculty of Natural Resources, Sari Agricultural and Natural Resources University (SANRU), Sari, Mazandaran P.O.BOX 48181-68984, Iran.
| | - Mahdi Panahi
- Department of Geophysics, Young Researchers and Elites Club, North Tehran Branch, Islamic Azad University, Tehran P.O. Box 19585/466, Iran.
| | - Baharin Bin Ahmad
- 10 Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia.
| | - Lee Saro
- Geological Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Korea.
- Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Korea.
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Khosravi K, Pham BT, Chapi K, Shirzadi A, Shahabi H, Revhaug I, Prakash I, Tien Bui D. A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Sci Total Environ 2018; 627:744-755. [PMID: 29426199 DOI: 10.1016/j.scitotenv.2018.01.266] [Citation(s) in RCA: 144] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 01/12/2018] [Accepted: 01/26/2018] [Indexed: 06/08/2023]
Abstract
Floods are one of the most damaging natural hazards causing huge loss of property, infrastructure and lives. Prediction of occurrence of flash flood locations is very difficult due to sudden change in climatic condition and manmade factors. However, prior identification of flood susceptible areas can be done with the help of machine learning techniques for proper timely management of flood hazards. In this study, we tested four decision trees based machine learning models namely Logistic Model Trees (LMT), Reduced Error Pruning Trees (REPT), Naïve Bayes Trees (NBT), and Alternating Decision Trees (ADT) for flash flood susceptibility mapping at the Haraz Watershed in the northern part of Iran. For this, a spatial database was constructed with 201 present and past flood locations and eleven flood-influencing factors namely ground slope, altitude, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), land use, rainfall, river density, distance from river, lithology, and Normalized Difference Vegetation Index (NDVI). Statistical evaluation measures, the Receiver Operating Characteristic (ROC) curve, and Freidman and Wilcoxon signed-rank tests were used to validate and compare the prediction capability of the models. Results show that the ADT model has the highest prediction capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively. These techniques have proven successful in quickly determining flood susceptible areas.
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Affiliation(s)
- Khabat Khosravi
- Department of Watershed Management Engineering, Faculty of Natural Resources, Sari Agricultural Science and Natural Resources University, Sari, Iran
| | - Binh Thai Pham
- Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
| | - Kamran Chapi
- Departments of Watershed Sciences Engineering, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran
| | - Ataollah Shirzadi
- Departments of Watershed Sciences Engineering, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran
| | - Himan Shahabi
- Departments of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran
| | - Inge Revhaug
- Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, PO-5003IMT, AAs, Norway
| | - Indra Prakash
- Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar, India
| | - Dieu Tien Bui
- Geographic Information System Group, Department of Business and IT, University College of Southeast Norway, Gulbringvegen 36, N-3800 Bø i Telemark, Norway
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Bui KTT, Tien Bui D, Zou J, Van Doan C, Revhaug I. A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2666-0] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Phillips BT, Wang ED, Lanier ST, Khan SU, Dagum AB, Bui DT. 88C: HOW LONG DO WE NEED POSTOPERATIVE ANTIBIOTIC PROPHYLAXIS FOR IMMEDIATE BREAST RECONSTRUCTION? Plast Reconstr Surg 2010. [DOI: 10.1097/01.prs.0000371824.42589.cb] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Lanier ST, Wang ED, Phillips BT, Khan SU, Dagum AB, Bui DT. 95C: THE ASSOCIATION BETWEEN CLOSED SUCTION DRAINAGE DURATION AND COMPLICATION RATES IN TISSUE EXPANDER/IMPLANT BREAST RECONSTRUCTION WITH ANTIBIOTIC PROPHYLAXIS. Plast Reconstr Surg 2010. [DOI: 10.1097/01.prs.0000371831.67245.1d] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Kato K, Kusagawa S, Motomura K, Yang R, Shiino T, Nohtomi K, Sato H, Shibamura K, Nguyen TH, Pham KC, Pham HT, Duong CT, Nguyen TH, Bui DT, Hoang TL, Nagai Y, Takebe Y. Closely related HIV-1 CRF01_AE variant among injecting drug users in northern Vietnam: evidence of HIV spread across the Vietnam-China border. AIDS Res Hum Retroviruses 2001; 17:113-23. [PMID: 11177391 DOI: 10.1089/08892220150217201] [Citation(s) in RCA: 36] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
To investigate the nature of recent HIV outbreaks among injecting drug users (IDUs) near the Vietnam-China border, we genetically analyzed 24 HIV-positive blood specimens from 2 northern provinces of Vietnam (Lang Son and quang Ninh) adjacent to the China border, where HIV outbreaks among IDUs were first detected in late 1996. Genetic subtyping based on gag (p17) and env (C2/V3) sequences revealed that CRF01_AE is a principal strain circulating throughout Vietnam, including the provinces near the China border. The majority of CRF01_AE sequences among IDUs in Quang Ninh and Lang Son showed significant clustering with those found in nearby Pingxiang City of China's Guangxi Province, sharing a unique valine substitution 12 amino acids downstream of the V3 loop. This particular subtype E variant, uniquely found among IDUs in northern Vietnam and southeastern China, is designated E(v). The genetic diversity of CRF01_AE distributed in Quang Ninh (1.5 +/- 0.6%) and Pingxiang City (1.9 +/- 1.2%) was remarkably low, indicating the emerging nature of HIV spread in these areas. It is also noted that the genetic diversity of CRF01_AE among IDUs was consistently lower than that in persons infected sexually, suggesting that fewer closely related CRF01_AE variants were introduced into IDUs and, conversely, that multiple strains of CRF01_AE had been introduced via the sexual route. The data in the present study provide additional evidence that HIV outbreaks among IDUs in northern Vietnam were caused by the recent introduction of a highly homogeneous CRF01_AE variant (E(v)) closely related to that prevailing in nearby southern China.
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Affiliation(s)
- K Kato
- Laboratory of Molecular Virology and Epidemiology, AIDS Research Center, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
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Kaplan RF, Fletcher JE, Hannallah RS, Bui DT, Slaven JS, Darrow EJ, Tsai KT. The potency (ED50) and cardiovascular effects of rapacuronium (Org 9487) during narcotic-nitrous oxide-propofol anesthesia in neonates, infants, and children. Anesth Analg 1999; 89:1172-6. [PMID: 10553829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
UNLABELLED We studied the neuromuscular blocking effects of rapacuronium (Org 9487) (dose-response curve, onset, and 50% effective dose [ED50] value), and changes in heart rate and blood pressure, as well as evidence of histamine release in neonates, infants, and children in an open-label, randomized, two-center study. Fifteen neonates, 30 infants, and 30 children were studied. Anesthesia was induced and maintained with propofol, nitrous oxide:oxygen (60:40), and fentanyl. Mechanomyographic monitoring of neuromuscular function was performed at the thumb. The potency (ED50) for neonates, infants, and children were 0.32 (95% confidence interval [CI] 0.15-0.61), 0.28 (95% CI 0.11-0.61), and 0.39 (95% CI 0.17-0.85) mg/kg, respectively. Neonates who received 0.3, 0.6, or 0.9 mg/kg Org 9487 developed a maximum T1 twitch depression of 34 +/-28%, 98 +/- 3%, and 99 +/- 2%, respectively. Time-to-peak effect (onset time) for 0.9 mg/kg Org 9487 was 57 +/- 20 s. Maximum percent T1 twitch depression (+/-SD) in infants who received 0.3, 0.6, or 0.9 mg/kg rapacuronium was 41 +/- 34%, 96 +/- 7%, and 100 +/- 1%, respectively. Time-to-peak effect for 0.9 mg/kg Org 9487 was 62 +/- 29 s. In children 0.3, 0.6, and 0.9 mg/kg rapacuronium resulted in an average percent T1 twitch suppression of 29 +/- 23, 83 +/- 11, and 90 +/- 16, respectively. Time-to-peak effect of 0.9 mg/kg Org 9487 was 96 +/- 33 s, respectively. There was no evidence of histamine release or significant changes in heart rate or blood pressure in either group at any dose. Rapacuronium is a low-potency nondepolarizing muscle relaxant with a fast onset of relaxation and minimal cardiovascular effects. Its potency (ED50) is similar in neonates (0.32 mg/kg), infants (0.28 mg/kg), and children (0.39 mg/kg). T1 suppression (90% +/- 16) is less and time to peak effect (96 +/- 33 s) is greater (0.9 mg/kg rapacuronium) in children, compared with the combined group of infants and neonates. IMPLICATIONS This study assesses the potency of rapacuronium (Org 9487) in pediatric patients. The potency of rapacuronium is similar in neonates (0.32 mg/kg), infants (0.28 mg/kg), and children (0.39 mg/kg).
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Affiliation(s)
- R F Kaplan
- Department of Anesthesiology, Children's National Medical Center, Washington, DC 20010, USA.
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Edwards DL, Arora CP, Bui DT, Castro LC. Long-term nitric oxide blockade in the pregnant rat: effects on blood pressure and plasma levels of endothelin-1. Am J Obstet Gynecol 1996; 175:484-8. [PMID: 8765273 DOI: 10.1016/s0002-9378(96)70166-7] [Citation(s) in RCA: 64] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Abnormalities in the production of nitric oxide and endothelin-1 have been implicated in the development of preeclampsia. We postulated that long-term nitric oxide synthase inhibition with L-nitro-arginine methyl ester would induce sustained hypertension, a rise in plasma levels of endothelin-1, and fetal growth restriction. STUDY DESIGN Conscious virgin and pregnant Sprague-Dawley rats received infusions of vehicle or L-nitro-arginine methyl ester (2.5 mg/kg/hr) for 11 days. Mean arterial pressure was assessed serially. On day 21 of gestation (or equivalent in virgin rats) plasma was collected for endothelin-1 levels; pup weight and litter size were determined. Data were analyzed with analysis of variance and regression techniques. RESULTS Mean arterial pressure was constant in virgin control rats (n = 7) but declined in pregnant control rats (n = 11) as gestation advanced. Nitric oxide synthase inhibition in virgin (n = 10) and pregnant (n = 11) rats caused sustained elevations in mean arterial pressure (165 +/- 7 vs 100 +/- 3 mm Hg, L-nitro-arginine methyl ester vs control virgin rats, p < 0.0001; 149 +/- 5 vs 91 +/- 2 mm Hg, L-nitro-arginine methyl ester vs control pregnant rats, p < 0.0001). L-nitro-arginine methyl ester induced a rise in plasma endothelin-1 levels in virgin (4.4 +/- 0.1 vs 3.5 +/- 0.1 pg/ml, L-nitro-arginine methyl ester vs control, p < 0.0001) and pregnant rats (3.0 +/- 0.1 vs 2.6 +/- 0.1 pg/ml, L-nitro-arginine methyl ester vs control, p < 0.0001). Pregnant rats had lower endothelin-1 levels than did virgin rats (p < 0.0001). Mean arterial pressure and endothelin-1 were significantly correlated in pregnant rats. L-nitro-arginine methyl ester decreased pup weight (2.4 +/- 0.4 vs 3.7 +/- 0.2 gm/pup/litter, L-nitro-arginine methyl ester vs control, p < 0.01) and litter size (6.6 +/- 1.3 vs 10.2 +/- 0.9 pups/litter, L-nitro-arginine methyl ester vs control, p < 0.05). CONCLUSIONS Long-term nitric oxide synthase blockade causes sustained hypertension, elevated levels of endothelin-1, and fetal growth restriction. Although the endocrine and pressor effects are not unique to pregnancy, this model clearly induces some of the changes seen in preeclampsia and may be useful for studying specific interventions.
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Affiliation(s)
- D L Edwards
- Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center Burns and Allen Research Institute, Los Angeles, California, USA
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Kamanna VS, Kashyap ML, Pai R, Bui DT, Jin FY, Roh DD, Shah GM, Kirschenbaum MA. Uremic serum subfraction inhibits apolipoprotein A-I production by a human hepatoma cell line. J Am Soc Nephrol 1994; 5:193-200. [PMID: 7993998 DOI: 10.1681/asn.v52193] [Citation(s) in RCA: 28] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Abnormalities in lipoprotein metabolism are common in uremic patients and may represent an additional risk factor for the development of atherosclerosis. Despite the frequent occurrence of lipoprotein abnormalities, the role of various serum toxins and subfractions that accumulate in uremic patients on lipoprotein metabolism is not clearly understood. This study addressed the role of uremic toxins on lipoprotein metabolism by examining the effect of a 500 to 2,000-d subfraction obtained from the serum of uremic and control subjects on the synthesis of apolipoprotein (apo) A-I in a human hepatoma cell line (Hep-G2). Serum subfractions obtained from uremic patients inhibited apo A-I synthesis and secretion by Hep-G2 cells in a dose-dependent manner as measured by (3H)leucine incorporation into apo A-I, immunoprecipitation, and ELISA. The uremic serum subfraction decreased the mRNA expression for apo A-I in Hep-G2 cells when compared with controls. These observations suggest that a component of uremic serum can have the potential to inhibit hepatic apo A-I synthesis and may adversely influence high-density lipoprotein metabolism, thus increasing the risk for the development of atherosclerotic vascular complications in uremic patients.
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Affiliation(s)
- V S Kamanna
- Section of Nephrology, Department of Veterans Affairs Medical Center, Long Beach, CA 90822
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