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Bayatavrkeshi M, Imteaz MA, Kisi O, Farahani M, Ghabaei M, Al-Janabi AMS, Hashim BM, Al-Ramadan B, Yaseen ZM. Drought trends projection under future climate change scenarios for Iran region. PLoS One 2023; 18:e0290698. [PMID: 37943868 PMCID: PMC10635549 DOI: 10.1371/journal.pone.0290698] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/15/2023] [Indexed: 11/12/2023] Open
Abstract
The study highlights the potential characteristics of droughts under future climate change scenarios. For this purpose, the changes in Standardized Precipitation Evapotranspiration Index (SPEI) under the A1B, A2, and B1 climate change scenarios in Iran were assessed. The daily weather data of 30 synoptic stations from 1992 to 2010 were analyzed. The HadCM3 statistical model in the LARS-WG was used to predict the future weather conditions between 2011 and 2112, for three 34-year periods; 2011-2045, 2046-2079, and 2080-2112. In regard to the findings, the upward trend of the potential evapotranspiration in parallel with the downward trend of the precipitation in the next 102 years in three scenarios to the base timescale was transparent. The frequency of the SPEI in the base month indicated that 17.02% of the studied months faced the drought. Considering the scenarios of climate change for three 34-year periods (i.e., 2011-2045, 2046-2079, and 2080-2112) the average percentages of potential drought occurrences for all the stations in the next three periods will be 8.89, 16.58, and 27.27 respectively under the B1 scenario. While the predicted values under the A1B scenario are 7.63, 12.66, and 35.08%respectively. The relevant findings under the A2 scenario are 6.73, 10.16, 40.8%. As a consequence, water shortage would be more serious in the third period of study under all three scenarios. The percentage of drought occurrence in the future years under the A2, B1, and A1B will be 19.23%, 17.74%, and 18.84%, respectively which confirms the worst condition under the A2 scenario. For all stations, the number of months with moderate drought was substantially more than severe and extreme droughts. Considering the A2 scenario as a high emission scenario, the analysis of SPEI frequency illustrated that the proportion of dry periods in regions with humid and cool climate is more than hot and warm climates; however, the duration of dry periods in warmer climates is longer than colder climates. Moreover, the temporal distribution of precipitation and potential evapotranspiration indicated that in a large number of stations, there is a significant difference between them in the middle months of the year, which justifies the importance of prudent water management in warm months.
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Affiliation(s)
- Maryam Bayatavrkeshi
- Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada
| | - Monzur Alam Imteaz
- Civil and Construction Engineering, Swinburne University of Technology, Melbourne, VIC, Australia
| | - Ozgur Kisi
- Department of Civil Engineering, Luebeck University of Applied Sciences, Lübeck, Germany
- Department of Civil Engineering, Ilia State University, Tbilisi, Georgia
| | | | - Mohammad Ghabaei
- Ministry of Energy, Iran Water Resources Management Co., Tehran, Iran
| | | | - Bassim Mohammed Hashim
- Environment, Water and Renewable Energy Directorate, Ministry of Science and Technology, Baghdad, Iraq
| | - Baqer Al-Ramadan
- Architecture & City Design Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
- Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
| | - Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
- Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
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Kisi O, Ajri S, Jörgens KC, Karande A, Kraus S, Naumann B, Nierman K, Seel W, Kulls C. Comments on "Splitting tensile strength prediction of sustainable high-performance concrete using machine learning techniques" by Wu, Yangi et al., https://doi.org/10.1007/s11356-022-22048-2. Environ Sci Pollut Res Int 2023; 30:109854-109855. [PMID: 37479937 PMCID: PMC10622343 DOI: 10.1007/s11356-023-28829-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 07/12/2023] [Indexed: 07/23/2023]
Affiliation(s)
- Ozgur Kisi
- Department of Civil Engineering, Lübeck University of Applied Sciences, 23562, Lübeck, Germany.
- Department of Civil Engineering, Ilia State University, 0162, Tbilisi, Georgia.
| | - Sara Ajri
- Department of Civil Engineering, Lübeck University of Applied Sciences, 23562, Lübeck, Germany
| | - Kim Cedric Jörgens
- Department of Civil Engineering, Lübeck University of Applied Sciences, 23562, Lübeck, Germany
| | - Arti Karande
- Department of Civil Engineering, Lübeck University of Applied Sciences, 23562, Lübeck, Germany
| | - Sabine Kraus
- Department of Civil Engineering, Lübeck University of Applied Sciences, 23562, Lübeck, Germany
| | - Benita Naumann
- Department of Civil Engineering, Lübeck University of Applied Sciences, 23562, Lübeck, Germany
| | - Kim Nierman
- Department of Civil Engineering, Lübeck University of Applied Sciences, 23562, Lübeck, Germany
| | - Wiebke Seel
- Department of Civil Engineering, Lübeck University of Applied Sciences, 23562, Lübeck, Germany
| | - Christoph Kulls
- Department of Civil Engineering, Lübeck University of Applied Sciences, 23562, Lübeck, Germany
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Adnan RM, Dai HL, Kisi O, Heddam S, Kim S, Kulls C, Zounemat-Kermani M. Modelling biochemical oxygen demand using improved neuro-fuzzy approach by marine predators algorithm. Environ Sci Pollut Res Int 2023; 30:94312-94333. [PMID: 37531049 PMCID: PMC10468928 DOI: 10.1007/s11356-023-28935-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/19/2023] [Indexed: 08/03/2023]
Abstract
Biochemical oxygen demand (BOD) is one of the most important parameters used for water quality assessment. Alternative methods are essential for accurately prediction of this parameter because the traditional method in predicting the BOD is time-consuming and it is inaccurate due to inconstancies in microbial multiplicity. In this study, the applicability of four hybrid neuro-fuzzy (ANFIS) methods, ANFIS with genetic algorithm (GA), ANFIS with particle swarm optimization (PSO), ANFIS with sine cosine algorithm (SCA), and ANFIS with marine predators algorithm (MPA), was investigated in predicting BOD using distinct input combinations such as potential of hydrogen (pH), dissolved oxygen (DO), electrical conductivity (EC), water temperature (WT), suspended solids (SS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (T-P) acquired from two river stations, Gongreung and Gyeongan, South Korea. The applicability of multi-variate adaptive regression spline (MARS) in determination of the best input combination was examined. The ANFIS-MPA was found to be the best model with the lowest root mean square error and mean absolute error and the highest determination coefficient. It improved the root mean square error of ANFIS-PSO, ANFIS-GA, and ANFIS-SCA models by 13.8%, 12.1%, and 6.3% for Gongreung Station and by 33%, 25%, and 6.3% for Gyeongan Station in the test stage, respectively.
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Affiliation(s)
- Rana Muhammad Adnan
- School of Economics and Statistics, Guangzhou University, Guangzhou, 510006 China
| | - Hong-Liang Dai
- School of Economics and Statistics, Guangzhou University, Guangzhou, 510006 China
| | - Ozgur Kisi
- Department of Civil Engineering, Lübeck University of Applied Science, 23562 Lubeck, Germany
- Department of Civil Engineering, School of Technology, Ilia State University, 0162 Tbilisi, Georgia
| | - Salim Heddam
- Faculty of Science, Agronomy Department, Hydraulics Division University, 20 Août 1955, Route El Hadaik, 21024 Skikda, BP 26 Algeria
| | - Sungwon Kim
- Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, 36040 Republic of Korea
| | - Christoph Kulls
- Department of Civil Engineering, Lübeck University of Applied Science, 23562 Lubeck, Germany
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Jafarzade N, Kisi O, Yousefi M, Baziar M, Oskoei V, Marufi N, Mohammadi AA. Viability of two adaptive fuzzy systems based on fuzzy c means and subtractive clustering methods for modeling Cadmium in groundwater resources. Heliyon 2023; 9:e18415. [PMID: 37520981 PMCID: PMC10382293 DOI: 10.1016/j.heliyon.2023.e18415] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [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: 01/12/2023] [Revised: 07/16/2023] [Accepted: 07/17/2023] [Indexed: 08/01/2023] Open
Abstract
The Adaptive Neuro-Fuzzy Inference System (ANFIS) combines the strengths of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) into a single framework. By doing so, it allows for quicker learning and adaptable interpretation capabilities, which are useful for modeling complex patterns and identifying nonlinear relationships. One significant challenge in assessing water quality is the difficulty and time-consuming nature of determining the various factors that impact it. Given this situation, predicting heavy metal levels in groundwater resources, both urban and rural, is essential. This paper investigates two methods, ANFIS-FCM and ANFIS-SUB, to determine their effectiveness in modeling Cadmium (Cd) in groundwater resources. The parameters to be considered are: dissolved solids (TDS), electroconductivity (EC), turbidity (TU), and pH were assumed to be the independent variables. A total of 51 sampling location were used with in the groundwater resource were used to develop the fuzzy models. For evaluating the performance of ANFIS-FCM and ANFIS-SUB models, three different performance criteria including the correlation coefficient, root mean square error, and sum square error were used for comparing the model outputs with actual outputs. Based on the obtained results from scatter plots of actual and predicted value by ANFIS-SUB and ANFIS- FCM models, the determination coefficient (R2) value for total data, test and train sets is equal to 0.978, 0.982, 0.993 and to 0.983, 0.999 and 0.998 respectively. This result proved the Cd predictions of the implemented ANFIS-FCM model was significantly close to the measured all experimental data with R2 of 0.983. The performance of the implemented ANFIS-FCM model was compared with the ANFIS-SUB model and it is found that the ANFIS-FCM provided slightly higher accuracy than the ANFIS-SUB model. Also, the results obtained from the comparison between the predicted and the actual data indicated that the ANFIS-FCM and ANFIS-SUB have a strong potential in estimating the heavy metals in the groundwater with a high degree of accuracy.
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Affiliation(s)
- Naghmeh Jafarzade
- Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ozgur Kisi
- Department of Civil Engineering, Technical University of Lübeck, 23562, Lübeck, Germany
- Department of Civil Engineering, Ilia State University, 0162, Tbilisi, Georgia
| | - Mahmood Yousefi
- Department of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
| | - Mansour Baziar
- Department of Environmental Health Engineering, Ferdows Faculty of Medical Sciences, Birjand University of Medical Sciences, Birjand, Iran
| | - Vahide Oskoei
- School of Life and Environmental Science, Deakin University, Geelong, Australia
| | - Nilufar Marufi
- Department of Environmental Health Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Akbar Mohammadi
- Department of Environmental Health Engineering, Neyshabur University of Medical Sciences, Neyshabur, Iran
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Samani S, Vadiati M, Nejatijahromi Z, Etebari B, Kisi O. Groundwater level response identification by hybrid wavelet-machine learning conjunction models using meteorological data. Environ Sci Pollut Res Int 2023; 30:22863-22884. [PMID: 36308648 DOI: 10.1007/s11356-022-23686-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 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: 05/18/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
Due to its heterogeneous and complex nature, groundwater modeling needs great effort to quantify the aquifer, a crucial tool for policymakers and hydrogeologists to understand the variations in groundwater levels (GWL). This study proposed a set of supervised machine learning (ML) models to delineate the GWL changes in the Zarand-Saveh complex aquifer in Iran using 15-year (2005-2020) monthly dataset. The wavelet transform (WT) procedure was also used to improve the GWL prediction ability of ML models for 3-month horizons using input datasets of precipitation, evapotranspiration, temperature, and GWL. The four well-accepted standalone ML methods, i.e., artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH), and least square support vector machine (LSSVM), were implemented and compared with the hybrid wavelet conjunction models. The methods were compared based on root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Nash-Sutcliffe efficiency (NSE). Comparison outcomes showed that the hybrid wavelet-ML considerably improved the standalone model results. The wavelet transform-least square support vector machine (WT-LSSVM) model was superior to other standalone and hybrid wavelet-ML methods to predict GWL. The best GWL predictions were acquired from the WT-LSSVM model with input scenario 5 involving all influential variables, and this model produced RMSE, MAE, R, and NSE as 0.05, 0.04, 0.99, and 0.99 for 1 month ahead of GWL prediction, while the corresponding values were obtained as 0.18, 0.14, 0.95, and 0.90 for 3 months ahead of GWL prediction, respectively.
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Affiliation(s)
- Saeideh Samani
- Department of Water Resources Study and Research, Water Research Institute (WRI), Tehran Province, District 4, Bahar Blvd, Tehran, Iran
| | - Meysam Vadiati
- Global Affairs, Hubert H. Humphrey Fellowship Program, University of California, 10 College Park, Davis, CA, 95616, USA.
| | - Zohre Nejatijahromi
- Department of Minerals and Hydrogeology, Faculty of Earth Sciences, Shahid Beheshti University, Evin Ave, Tehran, Iran
| | - Behrooz Etebari
- CalNRA/Dept. of Water Resources/ Sustainable Groundwater Management Office, 715 P Street, Sacramento, CA, USA
| | - Ozgur Kisi
- Department of Civil Engineering, Technical University of Lübeck, 23562, Lübeck, Germany
- Department of Civil Engineering, Ilia State University, 0162, Tbilisi, Georgia
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Dashti Z, Nakhaei M, Vadiati M, Karami GH, Kisi O. A literature review on pumping test analysis (2000-2022). Environ Sci Pollut Res Int 2023; 30:9184-9206. [PMID: 36454527 DOI: 10.1007/s11356-022-24440-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [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: 05/25/2022] [Accepted: 11/23/2022] [Indexed: 06/17/2023]
Abstract
Accurate and precise values of hydrodynamic parameters are needed for groundwater modeling and management. Pumping test in the aquifer is the standard method to estimate the transmissivity, hydraulic conductivity, and storage coefficient as the key hydrodynamic parameters. Analytical solutions with curve matching and numerical modeling are two methods to estimate these parameters in the aquifer. Graphical analyses are commonly applied to time-drawdown/water table data which are time-consuming and approximate. Graphical type-curve methods as promising tools are used extensively in water resources studies, while applying these methods is still new in pumping test analysis. In the current study, the first effort based on our knowledge, we have reviewed the literature type-curve graphical methods in pumping test analysis. To achieve this goal, we reviewed and compared the journal articles regarding the characteristics and capabilities of the modeling process from 2000 to 2022. We have clustered the reviewed papers into graphical, modeling, and hybrid categories. Then, a comprehensive review of the selected papers was presented to delineate the highlight of every paper. This review could guide researchers in pumping test analysis. Also, we have presented various recommendations for future research to improve the quality of hydrodynamic parameter estimation.
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Affiliation(s)
- Zahra Dashti
- Department of Applied Geology, Faculty of Earth Sciences, Kharazmi University, Tehran, Iran
| | - Mohammad Nakhaei
- Department of Applied Geology, Faculty of Earth Sciences, Kharazmi University, Tehran, Iran
| | - Meysam Vadiati
- Hubert H. Humphrey Fellowship Program, Global Affairs, University of California, 10 College Park, Davis, CA, 95616, USA.
| | - Gholam Hossein Karami
- Department of Applied Geology, Faculty of Earth Sciences, Kharazmi University, Tehran, Iran
| | - Ozgur Kisi
- Department of Architecture and Civil Engineering, University of Applied Sciences Lübeck, 23562, Lübeck, Germany
- Department of Civil Engineering, Ilia State University, 0162, Tbilisi, Georgia
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Ikram RMA, Hazarika BB, Gupta D, Heddam S, Kisi O. Streamflow prediction in mountainous region using new machine learning and data preprocessing methods: a case study. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-08163-8] [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: 12/28/2022]
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Heddam S, Ptak M, Sojka M, Kim S, Malik A, Kisi O, Zounemat-Kermani M. Least square support vector machine-based variational mode decomposition: a new hybrid model for daily river water temperature modeling. Environ Sci Pollut Res Int 2022; 29:71555-71582. [PMID: 35604598 DOI: 10.1007/s11356-022-20953-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [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: 01/07/2022] [Accepted: 05/16/2022] [Indexed: 06/15/2023]
Abstract
Machines learning models have recently been proposed for predicting rivers water temperature (Tw) using only air temperature (Ta). The proposed models relied on a nonlinear relationship between the Tw and Ta and they have proven to be robust modelling tools. The main motivation for this study was to evaluate how the variational mode decomposition (VMD) contributed to the improvement of machines learning performances for river Tw modelling. Measured data collected at five stations located in Poland from 1987 to 2014 were acquired and used for the analysis. Six machines learning models were used and compared namely, K-nearest neighbor's regression (KNNR), least square support vector machine (LSSVM), generalized regression neural network (GRNN), cascade correlation artificial neural networks (CCNN), relevance vector machine (RVM), and locally weighted polynomials regression (LWPR). The six models were developed according to three scenarios. First, the models were calibrated using only the Ta as input and obtained results show that the models were able to predict consistently water temperature, showing a high determination coefficient (R2) and Nash-Sutcliffe efficiency (NSE) with values near or above 0.910 and 0.915, respectively, and in overall the six models worked equally without clear superiority of one above another. Second, the air temperature was combined with the periodicity (i.e., day, month and year number) as input variable and a significant improvement was achieved. Both models show their ability to accurately predict river Tw with an overall accuracy of 0.956 for R2 and 0.955 for NSE values, but the LSSVM2 have some advantages such as a small errors metrics, and high fitting capabilities and it slightly surpasses the others models. Thirdly, air temperature was decomposed into several intrinsic mode functions (IMF) using the VMD method and the performances of the models were evaluated. The VMD parameters appeared to cause much influence on the prediction accuracy, exhibiting an improvement of about 40.50% and 39.12% in terms of RMSE and MAE between the first and the third scenarios, however, some models, i.e., GRNN and KNNR have not benefited from the VMD. This research has demonstrated the high capability of the VMD algorithm as a preprocessing approach in improving the accuracies of the machine learning models for river water temperature prediction.
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Affiliation(s)
- Salim Heddam
- Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Route El Hadaik, BP 26, Skikda, Algeria.
| | - Mariusz Ptak
- Department of Hydrology and Water Management, Adam Mickiewicz University, Krygowskiego 10, 61-680, Poznań, Poland
| | - Mariusz Sojka
- Department of Land Improvement, Environment Development and Spatial Management, Poznań University of Life Sciences, Piątkowska 94E, 60-649, Poznań, Poland
| | - Sungwon Kim
- Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, 36040, Republic of Korea
| | - Anurag Malik
- Regional Research Station, Punjab Agricultural University, Bathinda-151001, Punjab, India
| | - Ozgur Kisi
- Department of Civil Engineering, School of Technology, IIia State University, 0162, Tbilisi, Georgia
- Department of Civil Engineering, University of Applied Sciences, 23562, Lübeck, Germany
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Kim S, Alizamir M, Seo Y, Heddam S, Chung IM, Kim YO, Kisi O, Singh VP. Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction. Math Biosci Eng 2022; 19:12744-12773. [PMID: 36654020 DOI: 10.3934/mbe.2022595] [Citation(s) in RCA: 0] [Impact Index Per Article: 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] [Indexed: 06/17/2023]
Abstract
As an indicator measured by incubating organic material from water samples in rivers, the most typical characteristic of water quality items is biochemical oxygen demand (BOD5) concentration, which is a stream pollutant with an extreme circumstance of organic loading and controlling aquatic behavior in the eco-environment. Leading monitoring approaches including machine leaning and deep learning have been evolved for a correct, trustworthy, and low-cost prediction of BOD5 concentration. The addressed research investigated the efficiency of three standalone models including machine learning (extreme learning machine (ELM) and support vector regression (SVR)) and deep learning (deep echo state network (Deep ESN)). In addition, the novel double-stage synthesis models (wavelet-extreme learning machine (Wavelet-ELM), wavelet-support vector regression (Wavelet-SVR), and wavelet-deep echo state network (Wavelet-Deep ESN)) were developed by integrating wavelet transformation (WT) with the different standalone models. Five input associations were supplied for evaluating standalone and double-stage synthesis models by determining diverse water quantity and quality items. The proposed models were assessed using the coefficient of determination (R2), Nash-Sutcliffe (NS) efficiency, and root mean square error (RMSE). The significance of addressed research can be found from the overall outcomes that the predictive accuracy of double-stage synthesis models were not always superior to that of standalone models. Overall results showed that the SVR with 3th distribution (NS = 0.915) and the Wavelet-SVR with 4th distribution (NS = 0.915) demonstrated more correct outcomes for predicting BOD5 concentration compared to alternative models at Hwangji station, and the Wavelet-SVR with 4th distribution (NS = 0.917) was judged to be the most superior model at Toilchun station. In most cases for predicting BOD5 concentration, the novel double-stage synthesis models can be utilized for efficient and organized data administration and regulation of water pollutants on both stations, South Korea.
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Affiliation(s)
- Sungwon Kim
- Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, 36040, Republic of Korea
| | - Meysam Alizamir
- Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran
| | - Youngmin Seo
- Department of Constructional and Environmental Engineering, Kyungpook National University, Sangju, 37224, Republic of Korea
| | - Salim Heddam
- Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Route El Hadaik, BP 26, Skikda, Algeria
| | - Il-Moon Chung
- Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Republic of Korea
| | - Young-Oh Kim
- Department of Civil Engineering, Seoul National University, Seoul, Republic of Korea
| | - Ozgur Kisi
- Department of Civil Engineering, University of Applied Sciences, 23562, Lübeck, Germany
| | - Vijay P Singh
- Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A & M University, College Station, Texas, 77843-2117, USA
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Vadiati M, Rajabi Yami Z, Eskandari E, Nakhaei M, Kisi O. Application of artificial intelligence models for prediction of groundwater level fluctuations: case study (Tehran-Karaj alluvial aquifer). Environ Monit Assess 2022; 194:619. [PMID: 35904687 DOI: 10.1007/s10661-022-10277-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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: 10/26/2021] [Accepted: 07/04/2022] [Indexed: 06/15/2023]
Abstract
The nonlinear groundwater level fluctuations depend on the interaction of many factors such as evapotranspiration, precipitation, groundwater abstraction, and hydrogeological characteristics, making groundwater level prediction a complex task. Groundwater level changes are among the most critical issues in water resource management, which can be predicted to effectively provide management solutions to conserve renewable water resources. Understanding the aquifer status using numerical models is time-consuming and also is associated with inherent uncertainty; therefore, in recent decades, the application of artificial intelligence methods to predict water table fluctuations has significantly gained momentum. In this study, artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS), and least square support vector machine (SVM) methods were utilized to predict groundwater level (GWL) with 1-, 2-, and 3-month lead time in Tehran-Karaj plain. Several input scenarios were developed considering groundwater levels, average temperature, total precipitation, total evapotranspiration, and average river flow on a monthly interval. The four error criteria, the correlation coefficient (R), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and mean absolute error (MAE), were the basis to evaluate the models. Results showed that all the applied methods could provide acceptable GWL prediction, but the ANFIS was the most accurate. However, the ANFIS model showed slightly better performance by yielding R = 0.98 for the training stage and R = 0.98 for the testing stage in the P84 observation well and the second combination of inputs and 1-month lead time. The outcomes also revealed that all the approaches mentioned above could appropriately predict GWL for the leading time of 1 and 2 months, but the models provided unsatisfactory results for a 3-month leading time.
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Affiliation(s)
- Meysam Vadiati
- Global Affairs, Hubert H. Humphrey Fellowship Program, University of California, 10 College Park, Davis, CA, 95616, USA.
| | - Zahra Rajabi Yami
- Department of Applied Geology, Faculty of Earth Sciences, Kharazmi University, Tehran, Iran
| | - Effat Eskandari
- Department of Geology, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mohammad Nakhaei
- Department of Applied Geology, Faculty of Earth Sciences, Kharazmi University, Tehran, Iran
| | - Ozgur Kisi
- Department of Architecture and Civil Engineering, University of Applied Sciences Lübeck, 23562, Lübeck, Germany
- Department of Civil Engineering, Ilia State University, 0162, Tbilisi, Georgia
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Adnan RM, R. Mostafa R, Kisi O, Yaseen ZM, Shahid S, Zounemat-Kermani M. Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107379] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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12
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Kim S, Maleki N, Rezaie-Balf M, Singh VP, Alizamir M, Kim NW, Lee JT, Kisi O. Assessment of the total organic carbon employing the different nature-inspired approaches in the Nakdong River, South Korea. Environ Monit Assess 2021; 193:445. [PMID: 34173069 DOI: 10.1007/s10661-021-08907-4] [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: 11/02/2020] [Accepted: 01/26/2021] [Indexed: 06/13/2023]
Abstract
Total organic carbon (TOC) has vital significance for measuring water quality in river streamflow. The detection of TOC can be considered as an important evaluation because of issues on human health and environmental indicators. This research utilized the novel hybrid models to improve the predictive accuracy of TOC at Andong and Changnyeong stations in the Nakdong River, South Korea. A data pre-processing approach (i.e., complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)) and evolutionary optimization algorithm (i.e., crow search algorithm (CSA)) were implemented for enhancing the accuracy and robustness of standalone models (i.e., multivariate adaptive regression spline (MARS) and M5Tree). Various water quality indicators (i.e., TOC, potential of Hydrogen (pH), electrical conductivity (EC), dissolved oxygen (DO), water temperature (WT), chemical oxygen demand (COD), and suspended solids (SS)) were utilized for developing the standalone and hybrid models based on three input combinations (i.e., categories 1~3). The developed models were evaluated utilizing the correlation coefficient (CC), root-mean-square error (RMSE), and Nash-Sutcliffe efficiency (NSE). The CEEMDAN-MARS-CSA based on category 2 (C-M-CSA2) model (CC = 0.762, RMSE = 0.570 mg/L, and NSE = 0.520) was the most accurate for predicting TOC at Andong station, whereas the CEEMDAN-MARS-CSA based on category 3 (C-M-CSA3) model (CC = 0.900, RMSE = 0.675 mg/L, and NSE = 0.680) was the best at Changnyeong station.
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Affiliation(s)
- Sungwon Kim
- Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, 36040, South Korea.
| | - Niloofar Maleki
- Department of Civil Engineering, Pardisan University, Freidoonkenar, Iran
| | - Mohammad Rezaie-Balf
- Department of Civil Engineering, Graduate University of Advanced Technology-Kerman, P.O. Box 76315-116, Kerman, 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
- National Water Center, UAE University, Al Ain, UAE
| | - Meysam Alizamir
- Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran
| | - Nam Won Kim
- Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si, 10223, South Korea
| | - Jong-Tak Lee
- Department of Water Supply and Wastewater, Hando Engineering & Architecture, Daegu, 42140, South Korea
| | - Ozgur Kisi
- Department of Civil Engineering, School of Technology, Ilia State University, Tbilisi, Georgia
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam
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13
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Adnan RM, Khosravinia P, Karimi B, Kisi O. Prediction of hydraulics performance in drain envelopes using Kmeans based multivariate adaptive regression spline. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2020.107008] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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14
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Kazemi MH, Majnooni-Heris A, Kisi O, Shiri J. Generalized gene expression programming models for estimating reference evapotranspiration through cross-station assessment and exogenous data supply. Environ Sci Pollut Res Int 2021; 28:6520-6532. [PMID: 32996095 DOI: 10.1007/s11356-020-10916-8] [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: 07/07/2020] [Accepted: 09/17/2020] [Indexed: 06/11/2023]
Abstract
Adopting methodologies utilizing exogenous data from ancillary stations for determining crop water requirement is a suitable approach to exempt local shortcomings due to the lack of meteorological data/stations. Meanwhile, soft computing techniques might be suitable tools to be used with such data management scenarios. The present paper aimed at evaluating the generalizability of the gene expression programming (GEP) technique for estimating reference evapotranspiration (ET0) through cross-station assessment and exogenous data supply, using data from Turkey and Iran. The GEP-based models were established and learnt using data from 10 stations in Turkey, and then the developed models were tested (validated) in 18 stations of Iran with considerable latitude differences. Different time periods (beginning and the end of time series) were selected for the training and testing stations so that there was no overlap among the dates of the events in both the groups. A comparison was also performed between the GEP models and the corresponding commonly used empirical equations. The obtained results revealed that the generalized GEP models presented promising outcomes in simulating daily ET0 values when they were trained and tested in quite distant stations with different chronological periods of the applied parameters. The performance accuracy of the empirical equations calibrated using exogenous data was reduced in comparison with their original (non-calibrated) versions. Further, although the generalization ability of the GEP models was reduced when the climatic context of the training-testing stations was different, the overall performance accuracy of those models was higher than those of the commonly used classic empirical equations.
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Affiliation(s)
| | | | - Ozgur Kisi
- Civil Engineering Department, Ilia State University, Tbilisi, Georgia
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam
| | - Jalal Shiri
- Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.
- Center of Excellence in Hydro-informatics, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran.
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15
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Bayatvarkeshi M, Imteaz MA, Kisi O, Zarei M, Yaseen ZM. Application of M5 model tree optimized with Excel Solver Platform for water quality parameter estimation. Environ Sci Pollut Res Int 2021; 28:7347-7364. [PMID: 33033926 DOI: 10.1007/s11356-020-11047-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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: 06/12/2020] [Accepted: 09/28/2020] [Indexed: 06/11/2023]
Abstract
The high cost and time for determining water quality parameters justify the importance of application of mathematical models in discovering connection among them. This paper presents a data mining technique and its improved version in estimating water quality parameters. For this purpose, the surface and ground water quality data from Hamedan (Iran) between 2006 and 2015 were analyzed using M5 model tree and its modified version optimized with Excel Solver Platform (ESP). The values of electrical conductivity (EC), total dissolved solids (TDS), sodium adsorption ratio (SAR), and total hardness (TH) were considered as target variables, whereas pH, concentrations of sodium (Na), chlorine (Cl), bicarbonate (HCO3), sulfate (SO4), magnesium (Mg), calcium (Ca), and potassium (K) were as inputs. The results showed that in both the sources, pH was the least influential parameter on EC, TDS, SAR, and TH. It was found that among the objective parameters, the accuracy of models in estimating TH was higher than the other parameters, whereas SAR was a complex variable. The comparison of performances of the M5 and the M5-ESP models illustrated that the application of the ESP significantly decreased the normal root mean error (NRMSE) of the M5 model; the mean NRMSEs were decreased by 18.95% and 20.29% in estimating groundwater and surface water quality parameters, respectively. Moreover, ability of both the M5 and the M5-ESP models in computing objective parameters of the groundwater was found to be better than the surface water.
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Affiliation(s)
| | - Monzur Alam Imteaz
- Civil and Construction Engineering, Swinburne University of Technology, Melbourne, VIC, 3122, Australia
| | - Ozgur Kisi
- Civil Engineering Department, Ilia State University, Tbilisi, Georgia
- Institute of Research and Development , Duy Tan University , 550000, Da Nang, Vietnam
| | - Mahtab Zarei
- Department of Soil Science, Malayer University, Malayer, Iran
| | - Zaher Mundher Yaseen
- Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
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16
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Ehteram M, Ahmed AN, Latif SD, Huang YF, Alizamir M, Kisi O, Mert C, El-Shafie A. Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction. Environ Sci Pollut Res Int 2021; 28:1596-1611. [PMID: 32851519 DOI: 10.1007/s11356-020-10421-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [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: 04/17/2020] [Accepted: 08/06/2020] [Indexed: 06/11/2023]
Abstract
There is a need to develop an accurate and reliable model for predicting suspended sediment load (SSL) because of its complexity and difficulty in practice. This is due to the fact that sediment transportation is extremely nonlinear and is directed by numerous parameters such as rainfall, sediment supply, and strength of flow. Thus, this study examined two scenarios to investigate the effectiveness of the artificial neural network (ANN) models and determine the sensitivity of the predictive accuracy of the model to specific input parameters. The first scenario proposed three advanced optimisers-whale algorithm (WA), particle swarm optimization (PSO), and bat algorithm (BA)-for the optimisation of the performance of artificial neural network (ANN) in accurately predicting the suspended sediment load rate at the Goorganrood basin, Iran. In total, 5 different input combinations were examined in various lag days of up to 5 days to make a 1-day-ahead SSL prediction. Scenario 2 introduced a multi-objective (MO) optimisation algorithm that utilises the same inputs from scenario 1 as a way of determining the best combination of inputs. Results from scenario 1 revealed that high accuracy levels were achieved upon utilisation of a hybrid ANN-WA model over the ANN-BA with an RMSE value ranging from 1 to 6%. Furthermore, the ANN-WA model performed better than the ANN-PSO with an accuracy improvement value of 5-20%. Scenario 2 achieved the highest R2 when ANN-MOWA was introduced which shows that hybridisation of the multi-objective algorithm with WA and ANN model significantly improves the accuracy of ANN in predicting the daily suspended sediment load.
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Affiliation(s)
- Mohammad Ehteram
- Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
| | - Ali Najah Ahmed
- Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia
| | - Sarmad Dashti Latif
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia
| | - Yuk Feng Huang
- Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, Malaysia.
| | - Meysam Alizamir
- Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran
| | - Ozgur Kisi
- Department of Civil Engineering, Ilia State University, Tbilisi, Georgia
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam
| | - Cihan Mert
- Faculty of Computer Technologies and Engineering, International Black Sea University, Tbilisi, Georgia
| | - Ahmed El-Shafie
- Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603 Kuala Lumpur, Malaysia
- National Water Center, United Arab Emirates University (UAEU), 15551, Al Ain, United Arab Emirates
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17
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Adnan RM, Liang Z, Kisi O. Comments on "Predicting permeability changes with injecting CO2 in coal seams during CO2 geological sequestration: A comparative study among six SVM-based hybrid models" Science of the Total Environment, 705, 135941 (2020). Sci Total Environ 2020; 744:139486. [PMID: 32507510 DOI: 10.1016/j.scitotenv.2020.139486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 05/13/2020] [Accepted: 05/14/2020] [Indexed: 06/11/2023]
Abstract
In this study, some important mistakes related to model development process and missing information which should be carefully taken into account by the authors of the previous literature and other researchers are presented. Some important issues are presented to avoid propagation of similar mistakes in the scientific literature.
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Affiliation(s)
- Rana Muhammad Adnan
- College of Hydrology and Water Resources, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
| | - Zhongmin Liang
- College of Hydrology and Water Resources, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
| | - Ozgur Kisi
- Department of Civil Engineering, School of Technology, Ilia State University, Tbilisi, Georgia.
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18
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Tikhamarine Y, Malik A, Pandey K, Sammen SS, Souag-Gamane D, Heddam S, Kisi O. Monthly evapotranspiration estimation using optimal climatic parameters: efficacy of hybrid support vector regression integrated with whale optimization algorithm. Environ Monit Assess 2020; 192:696. [PMID: 33040211 DOI: 10.1007/s10661-020-08659-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [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: 05/04/2020] [Accepted: 10/05/2020] [Indexed: 06/11/2023]
Abstract
For effective planning of irrigation scheduling, water budgeting, crop simulation, and water resources management, the accurate estimation of reference evapotranspiration (ETo) is essential. In the current study, the hybrid support vector regression (SVR) coupled with Whale Optimization Algorithm (SVR-WOA) was employed to estimate the monthly ETo at Algiers and Tlemcen meteorological stations positioned in the north of Algeria under three different optimal input scenarios. Monthly climatic parameters, i.e., solar radiation (Rs), wind speed (Us), relative humidity (RH), and maximum and minimum air temperatures (Tmax and Tmin) of 14 years (2000-2013), were obtained from both stations. The accuracy of the hybrid SVR-WOA model was appraised against hybrid SVR-MVO (Multi-Verse Optimizer), and SVR-ALO (Ant Lion Optimizer) models through performance measures, i.e., mean absolute error (MAE), root-mean-square error (RMSE), index of scattering (IOS), index of agreement (IOA), Pearson correlation coefficient (PCC), Nash-Sutcliffe efficiency (NSE), and graphical interpretation (time-variation and scatter plots, radar chart, and Taylor diagram). The results showed that the SVR-WOA model performed superior to the SVR-MVO and SVR-ALO models at both stations in all scenarios. The SVR-WOA-1 model with five inputs (i.e., Tmin, Tmax, RH, Us, Rs: scenario-1) had the lowest value of MAE = 0.0658/0.0489 mm/month, RMSE = 0.0808/0.0617 mm/month, IOS = 0.0259/0.0165, and the highest value of NSE = 0.9949/0.9989, PCC = 0.9975/0.9995, and IOA = 0.9987/0.9997 for testing period at both stations, respectively. The proposed hybrid SVR-WOA model was found to be more appropriate and efficient in comparison to SVR-MVO and SVR-ALO models for estimating monthly ETo in the study region.
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Affiliation(s)
- Yazid Tikhamarine
- Department of Science and Technology, University of Tamanrasset, BP 10034 Sersouf, Tamanrasset, 11000, Algeria
- Leghyd Laboratory, Department of Civil Engineering, University of Sciences and Technology Houari Boumediene , BP 32 Al Alia, BP 32, Bab Ezzouar, Algiers, Algeria
| | - Anurag Malik
- Punjab Agricultural University, Regional Research Station, Bathinda, 151001, Punjab, India.
| | - Kusum Pandey
- Department of Soil and Water Engineering, Punjab Agricultural University, Ludhiana, 141004, Punjab, India
| | - Saad Shauket Sammen
- Department of Civil Engineering, College of Engineering, Diyala University, Baquba, Diyala 15 Governorate, Iraq
| | - Doudja Souag-Gamane
- Leghyd Laboratory, Department of Civil Engineering, University of Sciences and Technology Houari Boumediene , BP 32 Al Alia, BP 32, Bab Ezzouar, Algiers, Algeria
| | - Salim Heddam
- Faculty of Science, Agronomy Department, Hydraulics Division, University 20 Août 1955, Route EL HADAIK, BP 26, Skikda, Algeria
| | - Ozgur Kisi
- School of Technology, Ilia State University, Tbilisi, Georgia
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19
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Kisi O, Alizamir M, Trajkovic S, Shiri J, Kim S. Solar Radiation Estimation in Mediterranean Climate by Weather Variables Using a Novel Bayesian Model Averaging and Machine Learning Methods. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10350-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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20
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Kim S, Alizamir M, Zounemat-Kermani M, Kisi O, Singh VP. Assessing the biochemical oxygen demand using neural networks and ensemble tree approaches in South Korea. J Environ Manage 2020; 270:110834. [PMID: 32507742 DOI: 10.1016/j.jenvman.2020.110834] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [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: 02/17/2020] [Revised: 05/18/2020] [Accepted: 05/23/2020] [Indexed: 06/11/2023]
Abstract
The biochemical oxygen demand (BOD), one of widely utilized variables for water quality assessment, is metric for the ecological division in rivers. Since the traditional approach to predict BOD is time-consuming and inaccurate due to inconstancies in microbial multiplicity, alternative methods have been recommended for more accurate prediction of BOD. This study investigated the capability of a novel deep learning-based model, Deep Echo State Network (Deep ESN), for predicting BOD, based on various water quality variables, at Gongreung and Gyeongan stations, South Korea. The model was compared with the Extreme Learning Machine (ELM) and two ensemble tree models comprising the Gradient Boosting Regression Tree (GBRT) and Random Forests (RF). Diverse water quality variables (i.e., BOD, potential of Hydrogen (pH), electrical conductivity (EC), dissolved oxygen (DO), water temperature (WT), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (T-N), and total phosphorus (T-P)) were utilized for developing the Deep ESN, ELM, GBRT, and RF with five input combinations (i.e., Categories 1-5). These models were evaluated by root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), coefficient of determination (R2), and correlation coefficient (R). Overall evaluations suggested that the Deep ESN5 model provided the most reliable predictions of BOD among all the models at both stations.
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Affiliation(s)
- Sungwon Kim
- Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, 36040, Republic of Korea.
| | - Meysam Alizamir
- Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran.
| | | | - Ozgur Kisi
- Department of Civil Engineering, Ilia State University, Tbilisi, Georgia.
| | - Vijay P Singh
- Distinguished Professor and Caroline & William N. Lehrer Distinguished Chair in Water Engineering, Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A&M University, College Station, TX, 77843-2117, USA; National Water Center, UAE University, Al Ain, United Arab Emirates.
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21
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Sadeghi-Niaraki A, Kisi O, Choi SM. Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods. PeerJ 2020; 8:e8882. [PMID: 32864200 PMCID: PMC7430269 DOI: 10.7717/peerj.8882] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 03/10/2020] [Indexed: 11/20/2022] Open
Abstract
This paper investigates the capabilities of the evolutionary fuzzy genetic (FG) approach and compares it with three neuro-fuzzy methods—neuro-fuzzy with grid partitioning (ANFIS-GP), neuro-fuzzy with subtractive clustering (ANFIS-SC), and neuro-fuzzy with fuzzy c-means clustering (ANFIS-FCM)—in terms of modeling long-term air temperatures for sustainability based on geographical information. In this regard, to estimate long-term air temperatures for a 40-year (1970–2011) period, the models were developed using data for the month of the year, latitude, longitude, and altitude obtained from 71 stations in Turkey. The models were evaluated with respect to mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2). All data were divided into three parts and every model was tested on each. The FG approach outperformed the other models, enhancing the MAE, RMSE, NSE, and R2 of the ANFIS-GP model, which yielded the highest accuracy among the neuro-fuzzy models by 20%, 30%, and 4%, respectively. A geographical information system was used to obtain temperature maps using estimates of the optimal models, and the results of the model were assessed using it.
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Affiliation(s)
- Abolghasem Sadeghi-Niaraki
- Geoinformation Tech. Center of Excellence, Faculty of Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran
- Department of Computer Science and Engineering, Sejong University, Seoul, South Korea
| | - Ozgur Kisi
- Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia
| | - Soo-Mi Choi
- Department of Computer Science and Engineering, Sejong University, Seoul, South Korea
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22
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Tikhamarine Y, Malik A, Souag-Gamane D, Kisi O. Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration. Environ Sci Pollut Res Int 2020; 27:30001-30019. [PMID: 32445152 DOI: 10.1007/s11356-020-08792-3] [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] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 04/06/2020] [Indexed: 06/11/2023]
Abstract
Accurate estimation of reference evapotranspiration (ETo) is profoundly crucial in crop modeling, sustainable management, hydrological water simulation, and irrigation scheduling, since it accounts for more than two-thirds of global precipitation losses. Therefore, ETo-based estimation is a major concern in the hydrological cycle. The estimation of ETo can be determined using various methods, including field measurement (the scale of the lysimeter), experimental methods, and mathematical equations. The Food and Agriculture Organization recommended the Penman-Monteith (FAO-56 PM) method which was identified as the standard method of ETo estimation. However, this equation requires a large number of measured climatic data (maximum and minimum air temperature, relative humidity, solar radiation, and wind speed) that are not always available on meteorological stations. Over the decade, the artificial intelligence (AI) models have received more attention for estimating ETo on multi-time scales. This research explores the potential of new hybrid AI model, i.e., support vector regression (SVR) integrated with grey wolf optimizer (SVR-GWO) for estimating monthly ETo at Algiers, Tlemcen, and Annaba stations located in the north of Algeria. Five climatic variables namely relative humidity (RH), maximum and minimum air temperatures (Tmax and Tmin), solar radiation (Rs), and wind speed (Us) were used for model construction and evaluation. The proposed hybrid SVR-GWO model was compared against hybrid SVR-genetic algorithm (SVR-GA), SVR-particle swarm optimizer (SVR-PSO), conventional artificial neural network (ANN), and empirical (Turc, Ritchie, Thornthwaite, and three versions of Valiantzas methods) models by using root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), and Willmott index (WI), and through graphical interpretation. Through the results obtained, the performance of the SVR-GWO provides very promising and occasionally competitive results compared to other data-driven and empirical methods at study stations. Thus, the proposed SVR-GWO model with five climatic input variables outperformed the other models (RMSE = 0.0776/0.0613/0.0374 mm, NSE = 0.9953/ 0.9990/0.9995, PCC = 0.9978/0.9995/0.9998 and WI = 0.9988/0.9997/0.9999) for estimating ETo at Algiers, Tlemcen, and Annaba stations, respectively. In conclusion, the results of this research indicate the suitability of the proposed hybrid artificial intelligence model (SVR-GWO) at the study stations. Besides, promising results encourage researchers to transfer and test these models in other locations in the world in future works.
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Affiliation(s)
- Yazid Tikhamarine
- Leghyd Laboratory, Department of Civil Engineering, University of Sciences and Technology Houari Boumediene, BP 32 Al Alia, Babezzouar, Algiers, Algeria
| | - Anurag Malik
- Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India.
| | - Doudja Souag-Gamane
- Leghyd Laboratory, Department of Civil Engineering, University of Sciences and Technology Houari Boumediene, BP 32 Al Alia, Babezzouar, Algiers, Algeria
| | - Ozgur Kisi
- School of Technology, Ilia State University, Tbilisi, Georgia
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23
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Adnan RM, Kisi O. Transfer learning for neural network model in chlorophyll-a dynamics prediction by Wenchong Tian, Zhenliang Liao, and Xuan Wang. Environ Sci Pollut Res Int 2020; 27:30899-30900. [PMID: 32358752 DOI: 10.1007/s11356-020-09009-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 04/22/2020] [Indexed: 06/11/2023]
Affiliation(s)
- Rana Muhammad Adnan
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, 210098, China.
| | - Ozgur Kisi
- School of Technology, Ilia State University, Tbilisi, Georgia
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Adnan RM, Liang Z, Parmar KS, Soni K, Kisi O. Modeling monthly streamflow in mountainous basin by MARS, GMDH-NN and DENFIS using hydroclimatic data. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05164-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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25
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Muhammad Adnan R, Chen Z, Yuan X, Kisi O, El-Shafie A, Kuriqi A, Ikram M. Reference Evapotranspiration Modeling Using New Heuristic Methods. Entropy (Basel) 2020; 22:e22050547. [PMID: 33286320 PMCID: PMC7517042 DOI: 10.3390/e22050547] [Citation(s) in RCA: 20] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/02/2020] [Accepted: 05/05/2020] [Indexed: 11/16/2022]
Abstract
The study investigates the potential of two new machine learning methods, least-square support vector regression with a gravitational search algorithm (LSSVR-GSA) and the dynamic evolving neural-fuzzy inference system (DENFIS), for modeling reference evapotranspiration (ETo) using limited data. The results of the new methods are compared with the M5 model tree (M5RT) approach. Previous values of temperature data and extraterrestrial radiation information obtained from three stations, in China, are used as inputs to the models. The estimation exactness of the models is measured by three statistics: root mean square error, mean absolute error, and determination coefficient. According to the results, the temperature or extraterrestrial radiation-based LSSVR-GSA models perform superiorly to the DENFIS and M5RT models in terms of estimating monthly ETo. However, in some cases, a slight difference was found between the LSSVR-GSA and DENFIS methods. The results indicate that better prediction accuracy may be obtained using only extraterrestrial radiation information for all three methods. The prediction accuracy of the models is not generally improved by including periodicity information in the inputs. Using optimum air temperature and extraterrestrial radiation inputs together generally does not increase the accuracy of the applied methods in the estimation of monthly ETo.
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Affiliation(s)
- Rana Muhammad Adnan
- College of Hydrology and Water Resources, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; (R.M.A.); (M.I.)
| | - Zhihuan Chen
- School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
- Correspondence: (Z.C.); (X.Y.)
| | - Xiaohui Yuan
- School of Hydropower and Information Engineering, Huazhong University of Science & Technology, Wuhan 430074, China
- Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 443002, China
- Correspondence: (Z.C.); (X.Y.)
| | - Ozgur Kisi
- Department of Civil Engineering, School of Technology, Ilia State University, 0162 Tbilisi, Georgia;
| | - Ahmed El-Shafie
- Department of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia;
| | - Alban Kuriqi
- CERIS—Civil Engineering Research and Innovation for Sustainability, Instituto Superior Técnico—Universidade de Lisboa, 1049-001 Lisboa, Portugal;
| | - Misbah Ikram
- College of Hydrology and Water Resources, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; (R.M.A.); (M.I.)
- Department of Irrigation and Drainage, University of Agriculture, Faisalabad 38000, Pakistan
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Alizamir M, Kisi O, Ahmed AN, Mert C, Fai CM, Kim S, Kim NW, El-Shafie A. Advanced machine learning model for better prediction accuracy of soil temperature at different depths. PLoS One 2020; 15:e0231055. [PMID: 32287272 PMCID: PMC7156082 DOI: 10.1371/journal.pone.0231055] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 03/14/2020] [Indexed: 11/18/2022] Open
Abstract
Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four different depths. Various combinations of climatic variables are utilized as input to the developed models. The models’ outcomes are also compared with multi-linear regression based on Nash-Sutcliffe efficiency, root mean square error, and coefficient of determination statistics. ELM is found to be generally performs better than the other four alternatives in estimating soil temperatures. A decrease in performance of the models is observed by an increase in soil depth. It is found that soil temperatures at three depths (5, 10 and 50 cm) could be mapped utilizing only air temperature data as input while solar radiation and wind speed information are also required for estimating soil temperature at the depth of 100 cm.
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Affiliation(s)
- Meysam Alizamir
- Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran
- * E-mail:
| | - Ozgur Kisi
- Department of Civil Engineering, Ilia State University, Tbilisi, Georgia
| | - Ali Najah Ahmed
- Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional, Kajang, Selangor, Malaysia
| | - Cihan Mert
- Faculty of Computer Technologies and Engineering, International Black Sea University, Tbilisi, Georgia
| | - Chow Ming Fai
- Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional, Kajang, Selangor, Malaysia
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang, Selangor, Malaysia
| | - Sungwon Kim
- Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, Republic of Korea
| | - Nam Won Kim
- Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology, Goyang-daero, Ilsanseo-gu, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Ahmed El-Shafie
- Department of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur, Malaysia
- National Water Center, United Arab Emirates University, Al Ain, United Arab Emirates
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Kisi O, Alizamir M, Docheshmeh Gorgij A. Dissolved oxygen prediction using a new ensemble method. Environ Sci Pollut Res Int 2020; 27:9589-9603. [PMID: 31925684 DOI: 10.1007/s11356-019-07574-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [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: 06/05/2019] [Accepted: 12/29/2019] [Indexed: 06/10/2023]
Abstract
Prediction of dissolved oxygen which is an important water quality (WQ) parameter is crucial for aquatic managers who have responsibility for the ecosystem health's maintenance and for the management of reservoirs related to WQ. This study proposes a new ensemble method, Bayesian model averaging (BMA), for estimating hourly dissolved oxygen. The potential of the BMA was investigated and compared with five data-driven methods, extreme leaning machine (ELM), artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), classification and regression tree (CART), and multilinear regression (MLR), by considering hourly temperature, pH, and specific conductivity data as inputs. The methods were compared with respect to three statistics, root mean square errors (RMSE), Nash-Sutcliffe efficiency, and determination coefficient. Results based on two stations' data indicated that the proposed method performed superior to the ELM, ANN, ANFIS, CART, and MLR in estimation of hourly dissolved oxygen; corresponding improvements obtained by BMA are about 5-8%, 13-12%, 7-9%, and 18-27% with respect to RMSE. The ELM also outperformed the other four methods (ANN, ANFIS, CART, and MLR), and the CART and MLR indicated the lowest estimation accuracy in both stations. Examination of various input combinations revealed that the most effective variable is water temperature while the specific conductivity has negligible effect on hourly dissolved oxygen.
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Affiliation(s)
- Ozgur Kisi
- Department of Civil Engineering, Ilia State University, Tbilisi, Georgia
| | - Meysam Alizamir
- Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran.
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Malik A, Kumar A, Kisi O, Shiri J. Evaluating the performance of four different heuristic approaches with Gamma test for daily suspended sediment concentration modeling. Environ Sci Pollut Res Int 2019; 26:22670-22687. [PMID: 31172434 DOI: 10.1007/s11356-019-05553-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [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: 02/11/2019] [Accepted: 05/22/2019] [Indexed: 06/09/2023]
Abstract
Accurate prediction of suspended sediment concentration (SSC) carried by a river or watershed basin is essential for understanding the hydrology of basin in terms of water quality, river bed sustainability and aquatic habitats. In this study, four heuristic methods, namely, radial basis neural network (RBNN), self-organizing map neural network (SOMNN), least square support vector regression (LSSVR), and multivariate adaptive regression spline (MARS) were employed for daily SSC modeling at Ashti, Bamini, and Tekra stations located in Godavari River basin, Andhra Pradesh, India. The Gamma test (GT) was utilized for identifying the most significant input variables for the applied heuristic approaches. The results obtained by RBNN, SOMNN, LSSVR, and MARS models were compared with those of the traditional sediment rating curve (SRC). The performance of the models was evaluated based on the root mean square error (RMSE), coefficient of efficiency (COE), Pearson correlation coefficient (PCC), Willmott index (WI), and pooled average relative error (PARE) indices, as well as the visual inspection using line diagram, scatter diagram, and Taylor diagram (TD). The results of comparison revealed that the four heuristic methods gave higher accuracy than the SRC model. Among the heuristic models, the RBNN-3 (RMSE = 0.045, 0.062, 0.131 g/l; COE = 0.884, 0.883, 0.914; PCC = 0.955, 0.961, 0.958; and WI = 0.970, 0.963, 0.976) outperformed the other models in simulating daily SSC records in the studied stations.
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Affiliation(s)
- Anurag Malik
- Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar-263145, Uttarakhand, India.
| | - Anil Kumar
- Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar-263145, Uttarakhand, India
| | - Ozgur Kisi
- Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia
| | - Jalal Shiri
- Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
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Ehteram M, Singh VP, Ferdowsi A, Mousavi SF, Farzin S, Karami H, Mohd NS, Afan HA, Lai SH, Kisi O, Malek MA, Ahmed AN, El-Shafie A. An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration. PLoS One 2019; 14:e0217499. [PMID: 31150443 PMCID: PMC6544354 DOI: 10.1371/journal.pone.0217499] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 05/13/2019] [Indexed: 11/18/2022] Open
Abstract
Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET0 more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5-15% and 5-17% compared with the GP model, 12-21% and 10-22% compared with the M5T model, and 7-15% and 5-18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET0 values compared with the other models.
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Affiliation(s)
- Mohammad Ehteram
- Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
| | - Vijay P. Singh
- Department of Biological and Agricultural Engineering, Zachry Department of Civil Engineering, Texas A&M University, College Station, Texas, United States of America
| | - Ahmad Ferdowsi
- Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
| | - Sayed Farhad Mousavi
- Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
| | - Saeed Farzin
- Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
| | - Hojat Karami
- Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
| | - Nuruol Syuhadaa Mohd
- Department of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur, Malaysia
| | - Haitham Abdulmohsin Afan
- Department of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur, Malaysia
| | - Sai Hin Lai
- Department of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur, Malaysia
| | - Ozgur Kisi
- Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia
| | - M. A. Malek
- Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), Selangor, Malaysia
| | - Ali Najah Ahmed
- Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), Selangor, Malaysia
| | - Ahmed El-Shafie
- Department of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur, Malaysia
- * E-mail:
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Bayatvarkeshi M, Mohammadi K, Kisi O, Fasihi R. A new wavelet conjunction approach for estimation of relative humidity: wavelet principal component analysis combined with ANN. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3916-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Mohammadrezapour O, Kisi O, Pourahmad F. Fuzzy c-means and K-means clustering with genetic algorithm for identification of homogeneous regions of groundwater quality. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3768-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Zounemat-Kermani M, Ramezani-Charmahineh A, Adamowski J, Kisi O. Investigating the management performance of disinfection analysis of water distribution networks using data mining approaches. Environ Monit Assess 2018; 190:397. [PMID: 29900478 DOI: 10.1007/s10661-018-6769-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [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: 12/18/2017] [Accepted: 05/31/2018] [Indexed: 06/08/2023]
Abstract
Chlorination, the basic treatment utilized for drinking water sources, is widely used for water disinfection and pathogen elimination in water distribution networks. Thereafter, the proper prediction of chlorine consumption is of great importance in water distribution network performance. In this respect, data mining techniques-which have the ability to discover the relationship between dependent variable(s) and independent variables-can be considered as alternative approaches in comparison to conventional methods (e.g., numerical methods). This study examines the applicability of three key methods, based on the data mining approach, for predicting chlorine levels in four water distribution networks. ANNs (artificial neural networks, including the multi-layer perceptron neural network, MLPNN, and radial basis function neural network, RBFNN), SVM (support vector machine), and CART (classification and regression tree) methods were used to estimate the concentration of residual chlorine in distribution networks for three villages in Kerman Province, Iran. Produced water (flow), chlorine consumption, and residual chlorine were collected daily for 3 years. An assessment of the studied models using several statistical criteria (NSC, RMSE, R2, and SEP) indicated that, in general, MLPNN has the greatest capability for predicting chlorine levels followed by CART, SVM, and RBF-ANN. Weaker performance of the data-driven methods in the water distribution networks, in some cases, could be attributed to improper chlorination management rather than the methods' capability.
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Affiliation(s)
| | | | - Jan Adamowski
- Department of Bioresource Engineering, Faculty of Agriculture and Environmental Sciences, McGill University, Sainte-Anne-de-Bellevue, Quebec, Canada
| | - Ozgur Kisi
- Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia
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Karami H, Ehteram M, Mousavi SF, Farzin S, Kisi O, El-Shafie A. Optimization of energy management and conversion in the water systems based on evolutionary algorithms. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3412-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Heddam S, Kisi O. Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors. Environ Sci Pollut Res Int 2017; 24:16702-16724. [PMID: 28560629 DOI: 10.1007/s11356-017-9283-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [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: 11/29/2016] [Accepted: 05/17/2017] [Indexed: 06/07/2023]
Abstract
In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis activation function (R-ELM), online sequential extreme learning machine (OS-ELM), and optimally pruned extreme learning machine (OP-ELM), are newly applied for predicting dissolved oxygen concentration with and without water quality variables as predictors. Firstly, using data from eight United States Geological Survey (USGS) stations located in different rivers basins, USA, the S-ELM, R-ELM, OS-ELM, and OP-ELM were compared against the measured dissolved oxygen (DO) using four water quality variables, water temperature, specific conductance, turbidity, and pH, as predictors. For each station, we used data measured at an hourly time step for a period of 4 years. The dataset was divided into a training set (70%) and a validation set (30%). We selected several combinations of the water quality variables as inputs for each ELM model and six different scenarios were compared. Secondly, an attempt was made to predict DO concentration without water quality variables. To achieve this goal, we used the year numbers, 2008, 2009, etc., month numbers from (1) to (12), day numbers from (1) to (31) and hour numbers from (00:00) to (24:00) as predictors. Thirdly, the best ELM models were trained using validation dataset and tested with the training dataset. The performances of the four ELM models were evaluated using four statistical indices: the coefficient of correlation (R), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), and the mean absolute error (MAE). Results obtained from the eight stations indicated that: (i) the best results were obtained by the S-ELM, R-ELM, OS-ELM, and OP-ELM models having four water quality variables as predictors; (ii) out of eight stations, the OP-ELM performed better than the other three ELM models at seven stations while the R-ELM performed the best at one station. The OS-ELM models performed the worst and provided the lowest accuracy; (iii) for predicting DO without water quality variables, the R-ELM performed the best at seven stations followed by the S-ELM in the second place and the OP-ELM performed the worst with low accuracy; (iv) for the final application where training ELM models with validation dataset and testing with training dataset, the OP-ELM provided the best accuracy using water quality variables and the R-ELM performed the best at all eight stations without water quality variables. Fourthly, and finally, we compared the results obtained from different ELM models with those obtained using multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN). Results obtained using MLPNN and MLR models reveal that: (i) using water quality variables as predictors, the MLR performed the worst and provided the lowest accuracy in all stations; (ii) MLPNN was ranked in the second place at two stations, in the third place at four stations, and finally, in the fourth place at two stations, (iii) for predicting DO without water quality variables, MLPNN is ranked in the second place at five stations, and ranked in the third, fourth, and fifth places in the remaining three stations, while MLR was ranked in the last place with very low accuracy at all stations. Overall, the results suggest that the ELM is more effective than the MLPNN and MLR for modelling DO concentration in river ecosystems.
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Affiliation(s)
- Salim Heddam
- Faculty of Science, Agronomy Department, Hydraulics Division University, 20 Août 1955, Route El Hadaik, BP 26, Skikda, Algeria.
| | - Ozgur Kisi
- School of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia
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Adnan RM, Yuan X, Kisi O, Yuan Y. Streamflow forecasting of Astore River with Seasonal Autoregressive Integrated Moving Average model. ACTA ACUST UNITED AC 2017. [DOI: 10.19044/esj.2017.v13n12p145] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Simulation of streamflow is one of important factors in water utilization. In this paper, a linear statistical model i.e. Seasonal Autoregressive Integrated Moving Average model (SARIMA) is applied for modeling streamflow data of Astore River (1974 – 2010). On the basis of minimum Akaike Information Criteria Corrected (AICc) and Bayesian Information Criteria (BIC) values, the best model from different model structures has been identified. For testing period (2004-2010), the prediction accuracy of selected SARIMA model in comparison of auto regressive (AR) is evaluated on basis of root mean square error (RMSE), the mean absolute error (MAE) and coefficient of determination (R2 ). The results show that SARIMA performed better than AR model and can be used in streamflow forecasting at the study site.
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Mansouri I, Gholampour A, Kisi O, Ozbakkaloglu T. Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2492-4] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Karimi S, Shiri J, Kisi O, Shiri AA. Short-term and long-term streamflow prediction by using 'wavelet–gene expression' programming approach. ACTA ACUST UNITED AC 2015. [DOI: 10.1080/09715010.2015.1103201] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Yalcin H, Toker OS, Ozturk I, Dogan M, Kisi O. Prediction of fatty acid composition of vegetable oils based on rheological measurements using nonlinear models. EUR J LIPID SCI TECH 2012. [DOI: 10.1002/ejlt.201200040] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Ozturk I, Tornuk F, Sagdic O, Kisi O. Application of Non-linear Models to Predict Inhibition Effects of Various Plant Hydrosols onListeria monocytogenesInoculated on Fresh-Cut Apples. Foodborne Pathog Dis 2012; 9:607-16. [DOI: 10.1089/fpd.2012.1138] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
- Ismet Ozturk
- Department of Food Engineering, Engineering Faculty, Erciyes University, Kayseri, Turkey
| | - Fatih Tornuk
- Safiye Cikrikcioglu Vocational College, Erciyes University, Kayseri, Turkey
| | - Osman Sagdic
- Department of Food Engineering, Faculty of Chemical and Metallurgical Engineering, Yildiz Technical University, Istanbul, Turkey
| | - Ozgur Kisi
- Civil Engineering Department, Canik Basari University, Samsun, Turkey
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Yalcin H, Ozturk I, Karaman S, Kisi O, Sagdic O, Kayacier A. Prediction of Effect of Natural Antioxidant Compounds on Hazelnut Oil Oxidation by Adaptive Neuro-Fuzzy Inference System and Artificial Neural Network. J Food Sci 2011; 76:T112-20. [DOI: 10.1111/j.1750-3841.2011.02139.x] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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