1
|
Abidi JH, Elzain HE, Sabarathinam C, El Fehri RM, Farhat B, Ben Mammou A, Waterloo MJ, Yassin MA, Senapathi V. Integrated approach to understand the multiple natural and anthropogenic stresses on intensively irrigated coastal aquifer in the Mediterranean region. ENVIRONMENTAL RESEARCH 2024; 252:118757. [PMID: 38537744 DOI: 10.1016/j.envres.2024.118757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/29/2024] [Accepted: 03/19/2024] [Indexed: 04/06/2024]
Abstract
Understanding the major factors influencing groundwater chemistry and its evolution in irrigation areas is crucial for efficient irrigation management. Major ions and isotopes (δD-H2O together with δ18O-H2O) were used to identify the natural and anthropogenic factors contributing to groundwater salinization in the shallow aquifer of the Wadi Guenniche Plain (WGP) in the Mediterranean region of Tunisia. A comprehensive geochemical investigation of groundwater was conducted during both the low irrigation season (L-IR) and the high irrigation season (H-IR). The results show that the variation range and average concentrations of almost all the ions in both the L-IR and H-IR seasons are high. The groundwater in both seasons is characterized by high electrical conductivity and CaMgCl/SO4 and NaCl types. The dissolution of halite and gypsum, the precipitation of calcite and dolomite, and Na-Ca exchange are the main chemical reactions in the geochemical evolution of groundwater in the Wadi Guenniche Shallow Aquifer (WGSA). Stable isotopes of hydrogen and oxygen (δ18O-H2O and δD-H2O) indicate that groundwater in WGSA originated from local precipitation. In the H-IR season, the δ18O-H2O and δD-H2O values indicate that the groundwater experienced noticeable evaporation. The enriched isotopic signatures reveal that the WGSA's groundwater was influenced by irrigation return flow and seawater intrusion. The proportions of mixing with seawater were found to vary between 0.12% and 5.95%, and between 0.13% and 8.42% during the L-IR and H-IR seasons, respectively. Irrigation return flow and the associated evaporation increase the dissolved solids content in groundwater during the irrigation season. The long-term human activities (fertilization, irrigation, and septic waste infiltration) are the main drives of the high nitrate-N concentrations in groundwater. In coastal irrigation areas suffering from water scarcity, these results can help planners and policy makers understand the complexities of groundwater salinization to enable more sustainable management and development.
Collapse
Affiliation(s)
- Jamila Hammami Abidi
- Laboratory of Mineral Resources and Environment, Faculty of Sciences of Tunis, University of Tunis El Manar, 2092, Tunis, Tunisia
| | - Hussam Eldin Elzain
- Water Research Center, Sultan Qaboos University, PO Box 50, AlKhoud 123, Oman.
| | | | - Rihem Mejdoub El Fehri
- Laboratory of Geotechnical Engineering and Georisk, High National School of Engineering of Tunis, University of Tunis El Manar, 2092, Tunis, Tunisia
| | - Boutheina Farhat
- Laboratory of Mineral Resources and Environment, Faculty of Sciences of Tunis, University of Tunis El Manar, 2092, Tunis, Tunisia
| | - Abdallah Ben Mammou
- Laboratory of Mineral Resources and Environment, Faculty of Sciences of Tunis, University of Tunis El Manar, 2092, Tunis, Tunisia
| | | | - Mohamed A Yassin
- Interdisciplinary Research Center for Membranes and Water Security, KFUPM, 31261, Saudi Arabia; Department of Geosciences, College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
| | - Venkatramanan Senapathi
- PG and Research Department of Geology, National College (Autonomous), Tiruchirappalli - 620001, Tamil Nadu, India
| |
Collapse
|
2
|
Thanh NN, Chotpantarat S, Ngu NH, Thunyawatcharakul P, Kaewdum N. Integrating machine learning models with cross-validation and bootstrapping for evaluating groundwater quality in Kanchanaburi province, Thailand. ENVIRONMENTAL RESEARCH 2024; 252:118952. [PMID: 38636644 DOI: 10.1016/j.envres.2024.118952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 03/10/2024] [Accepted: 04/14/2024] [Indexed: 04/20/2024]
Abstract
Exploring the potential of new models for mapping groundwater quality presents a major challenge in water resource management, particularly in Kanchanaburi Province, Thailand, where groundwater faces contamination risks. This study aimed to explore the applicability of random forest (RF) and artificial neural networks (ANN) models to predict groundwater quality. Particularly, these two models were integrated into cross-validation (CV) and bootstrapping (B) techniques to build predictive models, including RF-CV, RF-B, ANN-CV, and ANN-B. Entropy groundwater quality index (EWQI) was converted to normalized EWQI which was then classified into five levels from very poor to very good. A total of twelve physicochemical parameters from 180 groundwater wells, including potassium, sodium, calcium, magnesium, chloride, sulfate, bicarbonate, nitrate, pH, electrical conductivity, total dissolved solids, and total hardness, were investigated to decipher groundwater quality in the eastern part of Kanchanaburi Province, Thailand. Our results indicated that groundwater quality in the study area was primarily polluted by calcium, magnesium, and bicarbonate and that the RF-CV model (RMSE = 0.06, R2 = 0.87, MAE = 0.04) outperformed the RF-B (RMSE = 0.07, R2 = 0.80, MAE = 0.04), ANN-CV (RMSE = 0.09, R2 = 0.70, MAE = 0.06), and ANN-B (RMSE = 0.10, R2 = 0.67, MAE = 0.06). Our findings highlight the superiority of the RF models over the ANN models based on the CV and B techniques. In addition, the role of groundwater parameters to the normalized EWQI in various machine learning models was found. The groundwater quality map created by the RF-CV model can be applied to orient groundwater use.
Collapse
Affiliation(s)
- Nguyen Ngoc Thanh
- University of Agriculture and Forestry, Hue University, 102 Phung Hung Str, Hue City, Thua Thien Hue, 53000, Viet Nam
| | - Srilert Chotpantarat
- Department of Geology, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand; Center of Excellence in Environmental Innovation and Management of Metals (EnvIMM), Environmental Research Institute, Chulalongkorn University, Phayathai Road, Pathumwan, Bangkok 10330, Thailand.
| | - Nguyen Huu Ngu
- University of Agriculture and Forestry, Hue University, 102 Phung Hung Str, Hue City, Thua Thien Hue, 53000, Viet Nam
| | - Pongsathorn Thunyawatcharakul
- International Postgraduate Program in Hazardous Substance and Environmental Management, Graduate School, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Narongsak Kaewdum
- Geoscience Program, Mahidol University Kanchanaburi Campus, Kanchanaburi, 71150, Thailand
| |
Collapse
|
3
|
Dhaoui O, Antunes IM, Benhenda I, Agoubi B, Kharroubi A. Groundwater salinization risk assessment using combined artificial intelligence models. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:33398-33413. [PMID: 38678534 DOI: 10.1007/s11356-024-33469-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 04/22/2024] [Indexed: 05/01/2024]
Abstract
Assessing the risk of groundwater contamination is of crucial importance for the management of water resources, particularly in arid regions such as Menzel Habib (south-eastern Tunisia). The aim of this research is to create and validate artificial intelligence models based on the original DRASTIC vulnerability methodology to explain groundwater salinization risk (GSR). To this end, several algorithms, such as artificial neural networks (ANN), support vector regression (SVR), and multiple linear regression (MLR), were applied to the Menzel Habib aquifer system. The results obtained indicate that the DRASTIC Vulnerability Index (VI) ranges from 91 to 141 and is classified into two categories: low and moderate vulnerability. However, the correlation between groundwater total dissolved solids (TDS) and the Vulnerability Index is relatively weak (r < 0.5). Indeed, the original DRASTIC index needs some improvements. To improve it, some adjustments are required, notably by incorporating the TDS-groundwater salinization risk (GSR) indicator. The seven parameters of the original DRASTIC model were used as inputs for the artificial intelligence models, while the GSR values were used as outputs. Performance indicators, such as the correlation coefficient (r) and the Willmott Agreement Index (d), showed that the ANN model outperformed the SVR and MLR models. Indeed, during the training phase, the ANN model obtained r values equal to 0.89 and d values of 0.4, demonstrating the superiority, robustness, and accuracy of ANN-based methodologies over the original DRASTIC model. The findings could provide valuable information to guide management of groundwater contamination risks, especially in arid regions.
Collapse
Affiliation(s)
- Oussama Dhaoui
- Higher Institute of Water Sciences and Techniques, Applied Hydrosciences Laboratory, University of Gabes, University Campus, 6033, Gabes, Tunisia.
- Institute of Earth Sciences, Pole of University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal.
| | - Isabel Margarida Antunes
- Institute of Earth Sciences, Pole of University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal
| | - Ines Benhenda
- Higher Institute of Water Sciences and Techniques, Applied Hydrosciences Laboratory, University of Gabes, University Campus, 6033, Gabes, Tunisia
| | - Belgacem Agoubi
- Higher Institute of Water Sciences and Techniques, Applied Hydrosciences Laboratory, University of Gabes, University Campus, 6033, Gabes, Tunisia
| | - Adel Kharroubi
- Higher Institute of Water Sciences and Techniques, Applied Hydrosciences Laboratory, University of Gabes, University Campus, 6033, Gabes, Tunisia
| |
Collapse
|
4
|
Hou Z, Lin Y, Liu T, Lu W. Bidirectional machine learning-assisted sensitivity-based stochastic searching approach for groundwater DNAPL source characterization. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:33591-33609. [PMID: 38684609 DOI: 10.1007/s11356-024-33405-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 04/16/2024] [Indexed: 05/02/2024]
Abstract
In this study, we designed a machine learning-based parallel global searching method using the Bayesian inversion framework for efficient identification of dense non-aqueous phase liquid (DNAPL) source characteristics and contaminant transport parameters in groundwater. Swarm intelligence organized hybrid-kernel extreme learning machine (SIO-HKELM) was proposed to approximate the forward and inverse input-output correlation with a high accuracy using the DNAPL transport numerical simulation model. An adaptive inverse-HKELM was established for preliminary estimation of the source characteristics and contaminant transport parameters to correct prior information and generate high-quality initial starting points of parallel searching. A local accurate forward-HKELM surrogate of the numerical model was embedded in the searching system for avoiding repetitive CPU-demanding likelihood evaluations. A sensitivity-based Metropolis criterion (MC), incorporating the dynamic particle swarm optimization (SD-PSO) algorithm, was developed for improving the search ergodicity and realizing precise inversion of all the unknown variables with drastic variations in sensitivity to the likelihood function. Results showed that the generalization capability and robustness of SIO-HKELM were superior to those of the traditional machine learning methods, including KELM and support vector regression (SVR), and it sufficiently approximated the forward and inverse input-output mapping of the numerical model with testing determination coefficients of 0.9944 and 0.6440, respectively. With high-quality prior information and initial starting points generated by the adaptive inverse-HKELM feed approach, the uncertainty in the inversion outputs was reduced, and the searching process rapidly converged to reasonable posterior distributions in around 60 iterations. Compared with the widely used multichain Markov chain Monte Carlo (MCMC) approach, the parallel searching lines generated by SD-PSO-MC adequately covered the searching space, and the "equifinality" effect was more effectively restrained by reducing the relative errors of all the point estimations to less than 8%. Therefore, the real source information reflected by the statistical characteristics of the SD-PSO-MC inversion outputs was more precise than that obtained using the multichain MCMC approach.
Collapse
Affiliation(s)
- Zeyu Hou
- Key Laboratory of Songliao Aquatic Environment, Ministry of Education, Jilin Jianzhu University, Changchun, 130118, China.
- School of Municipal and Environmental Engineering, Jilin Jianzhu University, Changchun, 130118, China.
| | - Yingzi Lin
- Key Laboratory of Songliao Aquatic Environment, Ministry of Education, Jilin Jianzhu University, Changchun, 130118, China
- School of Municipal and Environmental Engineering, Jilin Jianzhu University, Changchun, 130118, China
| | - Tongzhe Liu
- Shandong Institute of Geophysical & Geochemical Exploration, Jinan, 250000, China
- Shandong Provincial Engineering Research Center for Geological Prospecting, Jinan, 250000, China
| | - Wenxi Lu
- College of New Energy and Environment, Jilin University, Changchun, 130021, China
| |
Collapse
|
5
|
Bordbar M, Heggy E, Jun C, Bateni SM, Kim D, Moghaddam HK, Rezaie F. Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:24235-24249. [PMID: 38436856 DOI: 10.1007/s11356-024-32706-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 02/26/2024] [Indexed: 03/05/2024]
Abstract
Coastal aquifer vulnerability assessment (CAVA) studies are essential for mitigating the effects of seawater intrusion (SWI) worldwide. In this research, the vulnerability of the coastal aquifer in the Lahijan region of northwest Iran was investigated. A vulnerability map (VM) was created applying hydrogeological parameters derived from the original GALDIT model (OGM). The significance of OGM parameters was assessed using the mean decrease accuracy (MDA) method, with the current state of SWI emerging as the most crucial factor for evaluating vulnerability. To optimize GALDIT weights, we introduced the biogeography-based optimization (BBO) and gray wolf optimization (GWO) techniques to obtain to hybrid OGM-BBO and OGM-GWO models, respectively. Despite considerable research focused on enhancing CAVA models, efforts to modify the weights and rates of OGM parameters by incorporating deep learning algorithms remain scarce. Hence, a convolutional neural network (CNN) algorithm was applied to produce the VM. The area under the receiver-operating characteristic curves for OGM-BBO, OGM-GWO, and VMCNN were 0.794, 0.835, and 0.982, respectively. According to the CNN-based VM, 41% of the aquifer displayed very high and high vulnerability to SWI, concentrated primarily along the coastline. Additionally, 32% of the aquifer exhibited very low and low vulnerability to SWI, predominantly in the southern and southwestern regions. The proposed model can be extended to evaluate the vulnerability of various coastal aquifers to SWI, thereby assisting land use planers and policymakers in identifying at-risk areas. Moreover, deep-learning-based approaches can help clarify the associations between aquifer vulnerability and contamination resulting from SWI.
Collapse
Affiliation(s)
- Mojgan Bordbar
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania "Luigi Vanvitelli", Via Vivaldi 43, 81100, Caserta, Italy
| | - Essam Heggy
- Department of Electrical and Computer Engineering, Ming Hsieh, University of Southern California, 3737 Watt Way, PHE 502, Los Angeles, CA, 90089-0271, USA
- NASA Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA, 91109, USA
| | - Changhyun Jun
- Department of Civil and Environmental Engineering, College of Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Sayed M Bateni
- Department of Civil, Environmental, and Construction Engineering and Water Resources Research Center, University of Hawai'i at Manoa, Honolulu, HI, 96822, USA
| | - Dongkyun Kim
- Department of Civil Engineering, Hongik University, Mapo-Gu, Seoul, Republic of Korea
| | | | - Fatemeh Rezaie
- Department of Civil, Environmental, and Construction Engineering and Water Resources Research Center, University of Hawai'i at Manoa, Honolulu, HI, 96822, USA.
- Geoscience Data Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahak-Ro, Yuseong-Gu, Daejeon, 34132, Republic of Korea.
- Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-Ro, Yuseong-Gu, Daejeon, 34113, Republic of Korea.
| |
Collapse
|
6
|
Rad M, Abtahi A, Berndtsson R, McKnight US, Aminifar A. Interpretable machine learning for predicting the fate and transport of pentachlorophenol in groundwater. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 345:123449. [PMID: 38278404 DOI: 10.1016/j.envpol.2024.123449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 01/28/2024]
Abstract
Pentachlorophenol (PCP) is a commonly found recalcitrant and toxic groundwater contaminant that resists degradation, bioaccumulates, and has a potential for long-range environmental transport. Taking proper actions to deal with the pollutant accounting for the life cycle consequences requires a better understanding of its behavior in the subsurface. We recognize the huge potential for enhancing decision-making at contaminated groundwater sites with the arrival of machine learning (ML) techniques in environmental applications. We used ML to enhance the understanding of the dynamics of PCP transport properties in the subsurface, and to determine key hydrochemical and hydrogeological drivers affecting its transport and fate. We demonstrate how this complementary knowledge, provided by data-driven methods, may enable a more targeted planning of monitoring and remediation at two highly contaminated Swedish groundwater sites, where the method was validated. We evaluated 6 interpretable ML methods, 3 linear regressors and 3 non-linear (i.e., tree-based) regressors, to predict PCP concentration in the groundwater. The modeling results indicate that simple linear ML models were found to be useful in the prediction of observations for datasets without any missing values, while tree-based regressors were more suitable for datasets containing missing values. Considering that missing values are common in datasets collected during contaminated site investigations, this could be of significant importance for contaminated site planners and managers, ultimately reducing site investigation and monitoring costs. Furthermore, we interpreted the proposed models using the SHAP (SHapley Additive exPlanations) approach to decipher the importance of different drivers in the prediction and simulation of critical hydrogeochemical variables. Among these, sum of chlorophenols is of highest significance in the analyses. Setting that aside from the model, tetra chlorophenols, dissolved organic carbon, and conductivity found to be of highest importance. Accordingly, ML methods could potentially be used to improve the understanding of groundwater contamination transport dynamics, filling gaps in knowledge that remain when using more sophisticated deterministic modeling approaches.
Collapse
Affiliation(s)
- Mehran Rad
- Department of Agriculture and Food, Research Institutes of Sweden (RISE), Box 5401, SE-402 29, Göteborg, Sweden; Division of Water Resources Engineering, Department of Building and Environmental Technology, Lund University, Box 118, SE-221 00, Lund, Sweden.
| | - Azra Abtahi
- Department of Electrical and Information Technology, Lund University, Box 118, SE-221 00 Lund, Sweden
| | - Ronny Berndtsson
- Division of Water Resources Engineering, Department of Building and Environmental Technology, Lund University, Box 118, SE-221 00, Lund, Sweden; Centre for Advanced Middle Eastern Studies, Lund University, Box 201, SE-221 00, Lund, Sweden
| | - Ursula S McKnight
- Swedish Meteorological and Hydrological Institute, SE-601 76, Norrköping, Sweden
| | - Amir Aminifar
- Department of Electrical and Information Technology, Lund University, Box 118, SE-221 00 Lund, Sweden
| |
Collapse
|
7
|
Abduljaleel Y, Amiri M, Amen EM, Salem A, Ali ZF, Awd A, Lóczy D, Ghzal M. Enhancing groundwater vulnerability assessment for improved environmental management: addressing a critical environmental concern. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:19185-19205. [PMID: 38358629 PMCID: PMC10927854 DOI: 10.1007/s11356-024-32305-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 01/28/2024] [Indexed: 02/16/2024]
Abstract
Groundwater serves as a primary water source for various purposes. Therefore, aquifer pollution poses a critical threat to human health and the environment. Identifying the aquifer's highly vulnerable areas to pollution is necessary to implement appropriate remedial measures, thus ensuring groundwater sustainability. This paper aims to enhance groundwater vulnerability assessment (GWVA) to manage aquifer quality effectively. The study focuses on the El Orjane Aquifer in the Moulouya basin, Morocco, which is facing significant degradation due to olive mill wastewater. Groundwater vulnerability maps (GVMs) were generated using the DRASTIC, Pesticide DRASTIC, SINTACS, and SI methods. To assess the effectiveness of the proposed improvements, 24 piezometers were installed to measure nitrate concentrations, a common indicator of groundwater contamination. This study aimed to enhance GWVA by incorporating new layers, such as land use, and adjusting parameter rates based on a comprehensive sensitivity analysis. The results demonstrate a significant increase in Pearson correlation values (PCV) between the produced GVMs and measured nitrate concentrations. For instance, the PCV for the DRASTIC method improved from 0.42 to 0.75 after adding the land use layer and adjusting parameter rates using the Wilcoxon method. These findings offer valuable insights for accurately assessing groundwater vulnerability in areas with similar hazards and hydrological conditions, particularly in semi-arid and arid regions. They contribute to improving groundwater and environmental management practices, ensuring the long-term sustainability of aquifers.
Collapse
Affiliation(s)
- Yasir Abduljaleel
- Department of Civil and Environmental Engineering, Washington State University, Richland, WA, 99354, USA
| | - Mustapha Amiri
- Geomatics and Soil Management Laboratory, Faculty of Arts and Humanities, Université Mohammed Premier Oujda, 60000, Oujda, Morocco
| | - Ehab Mohammad Amen
- Natural Resources Research Center (NRRC), Tikrit University, Tikrit, 34001, Iraq
- Departamento de Geodinámica, Universidad de Granada, Granada, 18071, Spain
- Department of Applied Geology, Collage of Science, Tikrit University, Tikrit, 34001, Iraq
| | - Ali Salem
- Civil Engineering Department, Faculty of Engineering, Minia University, Minia, 61111, Egypt.
- Structural Diagnostics and Analysis Research Group, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány ut 2, 7624, Pecs, Hungary.
| | - Zana Fattah Ali
- Department of Geography, Faculty of Education, Koya University, Koysinjaq, 46011, Iraq
- Doctoral School of Earth Sciences, University of Pécs, Ifjúság útja 6, 7624, Pécs, Hungary
| | - Ahmed Awd
- Department of Food, Agriculture and Biological Engineering (FABE), The Ohio State University, Columbus, 43210, USA
- Egyptian Ministry of Water Resources and Irrigation (MWRI), Giza, 11925, Egypt
| | - Dénes Lóczy
- Institute of Geography and Earth Sciences, Faculty of Sciences, University of Pécs, Ifjúság útja 6, 7624, Pécs, Hungary
| | - Mohamed Ghzal
- Geomatics and Soil Management Laboratory, Faculty of Arts and Humanities, Université Mohammed Premier Oujda, 60000, Oujda, Morocco
| |
Collapse
|
8
|
Nandi R, Mondal S, Mandal J, Bhattacharyya P. From fuzzy-TOPSIS to machine learning: A holistic approach to understanding groundwater fluoride contamination. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169323. [PMID: 38104806 DOI: 10.1016/j.scitotenv.2023.169323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 11/22/2023] [Accepted: 12/10/2023] [Indexed: 12/19/2023]
Abstract
Fluoride (F-) contamination of groundwater is a prevalent environmental issue threatening public health worldwide and in India. This study targets an investigation into spatial distribution and contamination sources of fluoride in Dhanbad, India, to help develop tailored mitigation strategies. A triad of Multi Criteria Decision Making (MCDM) models (Fuzzy-TOPSIS), machine learning algorithms {logistic regression (LR), classification and regression tree (CART), Random Forest (RF)}, and classical methods has been undertaken here. Groundwater samples (n = 283) were collected for the purpose. Based on permissible limit (1.5 ppm) of fluoride in drinking water as set by the World Health Organization, samples were categorized as Unsafe (n = 67) and Safe (n = 216) groups. Mean fluoride concentration in Safe (0.63 ± 0.02 ppm) and Unsafe (3.69 ± 0.3 ppm) groups differed significantly (t-value = -10.04, p < 0.05). Physicochemical parameters (pH, electrical conductivity, total dissolved solids, total hardness, NO3-, HCO3-, SO42-, Cl-, Ca2+, Mg2+, K+, Na+ and F-) were recorded from samples of each group. The samples from 'Unsafe group' showed alkaline pH, the abundance of Na+ and HCO3- ions, prolonged rock water interaction in the aquifer, silicate weathering, carbonate dissolution, lack of Ca2+ and calcite precipitation which together facilitated the F- abundance. Aspatial distribution map of F- contamination was created, pinpointing the "contaminated pockets." Fuzzy- TOPSIS identified that samples from group Safe were closer to the ideal solution. Among these models, the LR proved superior, achieving the highest AUC score of 95.6 % compared to RF (91.3 %) followed by CART (69.4 %). This study successfully identified the primary contributors to F- contamination in groundwater and the developed models can help predicting fluoride contamination in other areas. The combination of different methodologies (Fuzzy-TOPSIS, machine learning algorithms, and classical methods) results in a synergistic effect where the strengths of each approach compensate for the limitations of the other.
Collapse
Affiliation(s)
- Rupsha Nandi
- Agricultural and Ecological Research Unit, Indian Statistical Institute, Giridih, Jharkhand 815301, India
| | - Sandip Mondal
- Department of Plant Pathology, The Ohio State University, OH, Columbus 43210, USA
| | - Jajati Mandal
- School of Sciences, Engineering & Environment, University of Salford, Manchester M5 4WT, UK
| | - Pradip Bhattacharyya
- Agricultural and Ecological Research Unit, Indian Statistical Institute, Giridih, Jharkhand 815301, India.
| |
Collapse
|
9
|
Elzain HE, Abdalla O, A Ahmed H, Kacimov A, Al-Maktoumi A, Al-Higgi K, Abdallah M, Yassin MA, Senapathi V. An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 351:119896. [PMID: 38171121 DOI: 10.1016/j.jenvman.2023.119896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/12/2023] [Accepted: 12/19/2023] [Indexed: 01/05/2024]
Abstract
Groundwater salinization in coastal aquifers is a major socioeconomic challenge in Oman and many other regions worldwide due to several anthropogenic activities and natural drivers. Therefore, assessing the salinization of groundwater resources is crucial to ensure the protection of water resources and sustainable management. The aim of this study is to apply a novel approach using predictive optimized ensemble trees-based (ETB) machine learning models, namely Catboost regression (CBR), Extra trees regression (ETR), and Bagging regression (BA), at two levels of modeling strategy for predicting groundwater TDS as an indicator for seawater intrusion in a coastal aquifer, Oman. At level 1, ETR and CBR models were used as base models or inputs for BA in level 2. The results show that the models at level 1 (i.e., ETR and CBR) yielded satisfactory results using a limited number of inputs (Cl, K, and Sr) from a few sets of 40 groundwater wells. The BA model at level 2 improved the overall performance of the modeling by extracting more information from ETR and CBR models at level 1 models. At level 2, the BA model achieved a significant improvement in accuracy (MSE = 0.0002, RSR = 0.062, R2 = 0.995 and NSE = 0.996) compared to each individual model of ETR (MSE = 0.0007, RSR = 0.245, R2 = 0.98 and NSE = 0.94), and CBR (MSE = 0.0035, RSR = 0.258, R2 = 0.933 and NSE = 0.934) at level 1 models in the testing dataset. BA model at level 2 outperformed all models regarding predictive accuracy, best generalization of new data, and matching the locations of the polluted and unpolluted wells. Our approach predicts groundwater TDS with high accuracy and thus provides early warnings of water quality deterioration along coastal aquifers which will improve water resources sustainability.
Collapse
Affiliation(s)
- Hussam Eldin Elzain
- Water Research Center, Sultan Qaboos University, P.O. 50, Al Khoudh 123, Oman.
| | - Osman Abdalla
- Department of Earth Sciences, College of Science, Sultan Qaboos University, P.O. 36, Al Khoudh 123, Oman.
| | - Hamdi A Ahmed
- Department of Industrial and Data Engineering, Pukyong National University, Busan, 48513, South Korea.
| | - Anvar Kacimov
- Department of Soils, Water and Agricultural Engineering, Sultan Qaboos University, P.O. 34, Al Khoudh 123, Oman.
| | - Ali Al-Maktoumi
- Water Research Center, Sultan Qaboos University, P.O. 50, Al Khoudh 123, Oman; Department of Soils, Water and Agricultural Engineering, Sultan Qaboos University, P.O. 34, Al Khoudh 123, Oman.
| | - Khalifa Al-Higgi
- Department of Earth Sciences, College of Science, Sultan Qaboos University, P.O. 36, Al Khoudh 123, Oman.
| | - Mohammed Abdallah
- College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China.
| | - Mohamed A Yassin
- Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
| | | |
Collapse
|
10
|
Chen K, Liu Q, Yang T, Ju Q, Zhu M. Risk assessment of nitrate groundwater contamination using GIS-based machine learning methods: A case study in the northern Anhui plain, China. JOURNAL OF CONTAMINANT HYDROLOGY 2024; 261:104300. [PMID: 38242063 DOI: 10.1016/j.jconhyd.2024.104300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 01/04/2024] [Accepted: 01/11/2024] [Indexed: 01/21/2024]
Abstract
Long-term agricultural activities have affected the sustainable development of groundwater in the Northern Anhui Plain, East China. It is, therefore, important to identify areas at high groundwater pollution risk in the Northern Anhui Plain to ensure effective protection of regional water resources. In this study, 60 groundwater samples were collected from the shallow aquifer of the plain and analyzed for nitrate (NO3-) concentrations. In addition, 10 environmental and geological factors including the elevations, distances-to-rivers, slope angles, orientations of slopes, land cover types, topographic wetness index (TWI), geomorphology, lithology, soil types, and precipitation amounts in the study area were selected as input layers. The light gradient boosting machine (LightGBM) and random forest (RF) algorithms, combined with the geographic information system (GIS), were performed to generate the groundwater pollution occurrence probability maps. The descriptive statistics showed that the NO3- concentrations in the shallow groundwater ranged from 4.3 to 73.6 mg/L. Most sampling wells exhibited NO3- concentrations above the threshold of 18.3 mg/L. The prediction results of the LightGBM and RF algorithms indicated a high groundwater NO3- pollution risk in the southern part of the plain. However, the LightGBM algorithm had a better prediction performance than RF, with a higher Kappa value of 0.84. Moreover, the frequency ratio method revealed that the precipitation amounts contributed to the groundwater NO3- pollution risk in the study area by 38.14%, followed by the elevations, slope angles, TWI, land cover types, and slope aspects, with contributions of 21.4, 13.02, 8.37, 7.44, and 6.51%, respectively. In the future, sampling of additional wells and further anthropogenic factors shall be considered for the development of more effective groundwater nitrate pollution prevention strategies provided to decision makers.
Collapse
Affiliation(s)
- Kai Chen
- School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, China; State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science & Technology, Huainan 232001, China
| | - Qimeng Liu
- School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, China.
| | - Tingting Yang
- School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, China
| | - Qiding Ju
- School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, China
| | - Mingfei Zhu
- School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, China
| |
Collapse
|
11
|
Uddin MG, Imran MH, Sajib AM, Hasan MA, Diganta MTM, Dabrowski T, Olbert AI, Moniruzzaman M. Assessment of human health risk from potentially toxic elements and predicting groundwater contamination using machine learning approaches. JOURNAL OF CONTAMINANT HYDROLOGY 2024; 261:104307. [PMID: 38278020 DOI: 10.1016/j.jconhyd.2024.104307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/10/2024] [Accepted: 01/18/2024] [Indexed: 01/28/2024]
Abstract
The Rooppur Nuclear Power Plant (RNPP) at Ishwardi, Bangladesh is planning to go into operation within 2024 and therefore, adjacent areas of RNPP is gaining adequate attention from the scientific community for environmental monitoring purposes especially for water resources management. However, there is a substantial lack of literature as well as environmental datasets for earlier years since very little was done at the beginning of the RNPP's construction phase. Therefore, this study was conducted to assess the potential toxic elements (PTEs) contamination in the groundwater and its associated health risk for residents at the adjacent part of the RNPP during the year of 2014-2015. For the purposes of achieving the aim of the study, groundwater samples were collected seasonally (dry and wet season) from nine sampling sites and afterwards analyzed for water quality indicators such as temperature (Temp.), pH, electrical conductivity (EC), total dissolved solid (TDS), total hardness (TH) and for PTEs including Iron (Fe), Manganese (Mn), Copper (Cu), Lead (Pb), Chromium (Cr), Cadmium (Cd) and Arsenic (As). This study adopted the newly developed Root Mean Square water quality index (RMS-WQI) model to assess the scenario of contamination from PTEs in groundwater whereas the human health risk assessment model was utilized to quantify the risk of toxicity from PTEs. In most of the sampling sites, PTEs concentration was found higher during the wet season than the dry season and Fe, Mn, Cd and As exceeded the guideline limit for drinking water. The RMS score mostly classified the groundwater in terms of PTEs contamination into "Fair" condition. The non-carcinogenic risks (expressed as Hazard Index-HI) revealed that around 44% and 89% of samples for adults and 67% and 100% of samples for children exceeded the threshold limit set by USEPA (HI > 1) and possessed risks through the oral pathway during dry and wet season, respectively. Furthermore, the calculated cumulative HI score was found higher for children than the adults throughout the study period. In terms of carcinogenic risk (CR) from PTEs, the magnitude of risk decreased following the pattern of Cr > As > Cd. Although the current study is based on old dataset, the findings might serve as a baseline for monitoring purposes to reduce future hazardous impact from the power plant.
Collapse
Affiliation(s)
- Md Galal Uddin
- Civil Engineering, School of Engineering, College of Science and Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland; Department of Geography and Environment, Jagannath University, Dhaka, Bangladesh.
| | - Md Hasan Imran
- Department of Environmental Science and Resource Management, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh
| | - Abdul Majed Sajib
- Civil Engineering, School of Engineering, College of Science and Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland
| | - Md Abu Hasan
- Bangladesh Reference institute for Chemical Measurements (BRiCM), Dr. Qudrat-e-Khuda Road, Dhanmondi, Dhaka 1205, Bangladesh
| | - Mir Talas Mahammad Diganta
- Civil Engineering, School of Engineering, College of Science and Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland
| | | | - Agnieszka I Olbert
- Civil Engineering, School of Engineering, College of Science and Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland
| | - Md Moniruzzaman
- Department of Geography and Environment, Jagannath University, Dhaka, Bangladesh
| |
Collapse
|
12
|
Saha A, Pal SC, Islam ARMT, Islam A, Alam E, Islam MK. Hydro-chemical based assessment of groundwater vulnerability in the Holocene multi-aquifers of Ganges delta. Sci Rep 2024; 14:1265. [PMID: 38218993 PMCID: PMC10787756 DOI: 10.1038/s41598-024-51917-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/11/2024] [Indexed: 01/15/2024] Open
Abstract
Determining the degree of high groundwater arsenic (As) and fluoride (F-) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F- concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F- contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.
Collapse
Affiliation(s)
- Asish Saha
- Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal, 713104, India
| | - Subodh Chandra Pal
- Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal, 713104, India.
| | - Abu Reza Md Towfiqul Islam
- Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh
- Department of Development Studies, Daffodil International University, Dhaka, 1216, Bangladesh
| | - Aznarul Islam
- Department of Geography, Aliah University, 17 Gorachand Road, Kolkata, 700014, India
| | - Edris Alam
- Faculty of Resilience, Rabdan Academy, 22401, Abu Dhabi, United Arab Emirates
- Department of Geography and Environmental Studies, University of Chittagong, Chittagong, 4331, Bangladesh
| | - Md Kamrul Islam
- Department of Civil and Environmental Engineering College of Engineering, King Faisal University, 31982, AlAhsa, Saudi Arabia
| |
Collapse
|
13
|
Saha A, Pal SC. Modelling groundwater vulnerability in a vulnerable deltaic coastal region of Sundarban Biosphere Reserve, India. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2023; 46:8. [PMID: 38142251 DOI: 10.1007/s10653-023-01799-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 10/31/2023] [Indexed: 12/25/2023]
Abstract
Groundwater is the most reliable source of freshwater for human well-being. Significant toxic contamination in groundwater, particularly in the aquifers of the Ganges delta, has been a substantial source of arsenic (As). The Sundarban Biosphere Reserve (SBR), located in the southwestern part of the world's largest Ganges delta, suffers from As contamination in groundwater. Therefore, assessment of groundwater vulnerability is essential to ensure the safety of groundwater quality in SBR. Three data-driven algorithms, i.e. "logistic regression (LR)", "random forest (RF)", and "boosted regression tree (BRT)", were used to assess groundwater vulnerability. Groundwater quality and hydrogeochemical characteristics were evaluated by Piper, United States Salinity Laboratory (USSL), and Wilcox's diagram. The result of this study indicates that among the applied models, BRT (AUC = 0.899) is the best-fit model, followed by RF (AUC = 0.882) and LR (AUC = 0.801) to assess groundwater vulnerability. In addition, the result also indicates that the general quality of the groundwater in this area is not very good for drinking purposes. The applied methods of this study can be used to evaluate the groundwater vulnerability of the other aquifer systems.
Collapse
Affiliation(s)
- Asish Saha
- Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal, 713104, India
| | - Subodh Chandra Pal
- Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal, 713104, India.
| |
Collapse
|
14
|
Mahlknecht J, Torres-Martínez JA, Kumar M, Mora A, Kaown D, Loge FJ. Nitrate prediction in groundwater of data scarce regions: The futuristic fresh-water management outlook. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 905:166863. [PMID: 37690767 DOI: 10.1016/j.scitotenv.2023.166863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/28/2023] [Accepted: 09/03/2023] [Indexed: 09/12/2023]
Abstract
Nitrate contamination in groundwater poses a significant threat to water quality and public health, especially in regions with limited data availability. This study addresses this challenge by employing machine learning (ML) techniques to predict nitrate (NO3--N) concentrations in Mexico's groundwater. Four ML algorithms-Extreme Gradient Boosting (XGB), Boosted Regression Trees (BRT), Random Forest (RF), and Support Vector Machines (SVM)-were executed to model NO3--N concentrations across the country. Despite data limitations, the ML models achieved robust predictive performances. XGB and BRT algorithms demonstrated superior accuracy (0.80 and 0.78, respectively). Notably, this was achieved using ∼10 times less information than previous large-scale assessments. The novelty lies in the first-ever implementation of the 'Support Points-based Split Approach' during data pre-processing. The models considered initially 68 covariates and identified 13-19 significant predictors of NO3--N concentration spanning from climate, geomorphology, soil, hydrogeology, and human factors. Rainfall, elevation, and slope emerged as key predictors. A validation incorporated nationwide waste disposal sites, yielding an encouraging correlation. Spatial risk mapping unveiled significant pollution hotspots across Mexico. Regions with elevated NO3--N concentrations (>10 mg/L) were identified, particularly in the north-central and northeast parts of the country, associated with agricultural and industrial activities. Approximately 21 million people, accounting for 10 % of Mexico's population, are potentially exposed to elevated NO3--N levels in groundwater. Moreover, the NO3--N hotspots align with reported NO3--N health implications such as gastric and colorectal cancer. This study not only demonstrates the potential of ML in data-scarce regions but also offers actionable insights for policy and management strategies. Our research underscores the urgency of implementing sustainable agricultural practices and comprehensive domestic waste management measures to mitigate NO3--N contamination. Moreover, it advocates for the establishment of effective policies based on real-time monitoring and collaboration among stakeholders.
Collapse
Affiliation(s)
- Jürgen Mahlknecht
- Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Campus Monterrey, Eugenio Garza Sada 2501, Monterrey, NL 64849, Mexico
| | - Juan Antonio Torres-Martínez
- Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Campus Monterrey, Eugenio Garza Sada 2501, Monterrey, NL 64849, Mexico.
| | - Manish Kumar
- Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Campus Monterrey, Eugenio Garza Sada 2501, Monterrey, NL 64849, Mexico; Sustainability Cluster, School of Advanced Engineering, UPES, Dehradun, Uttarakhand 248007, India
| | - Abrahan Mora
- Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Campus Puebla, Atlixcáyotl 5718, Puebla de Zaragoza, Puebla 72453, Mexico
| | - Dugin Kaown
- School of Earth and Environmental Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, South Korea
| | - Frank J Loge
- Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Campus Monterrey, Eugenio Garza Sada 2501, Monterrey, NL 64849, Mexico; Department of Civil and Environmental Engineering, University of California Davis, One Shields Avenue, Davis, CA 95616, USA
| |
Collapse
|
15
|
Nadiri AA, Bordbar M, Nikoo MR, Silabi LSS, Senapathi V, Xiao Y. Assessing vulnerability of coastal aquifer to seawater intrusion using Convolutional Neural Network. MARINE POLLUTION BULLETIN 2023; 197:115669. [PMID: 37922752 DOI: 10.1016/j.marpolbul.2023.115669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 10/06/2023] [Accepted: 10/11/2023] [Indexed: 11/07/2023]
Abstract
This study examined coastal aquifer vulnerability to seawater intrusion (SWI) in the Shiramin area in northwest Iran. Here, six types of hydrogeological data layers existing in the traditional GALDIT framework (TGF) were used to build one vulnerability map. Moreover, a modified traditional GALDIT framework (mod-TGF) was prepared by eliminating the data layer of aquifer type from the GALDIT model and adding the data layers of aquifer media and well density. To the best of our knowledge, there is a research gap to improve the TGF using deep learning algorithms. Therefore, this research adopted the Convolutional Neural Network (CNN) as a new deep learning algorithm to improve the mod-TGF framework for assessing the coastal aquifer vulnerability. Based on the findings, the CNN model could increase the performance of the mod-TGF by >30 %. This research can be a reference for further aquifer vulnerability studies.
Collapse
Affiliation(s)
- Ata Allah Nadiri
- Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran; Medical Geology and Environment Research Center, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran; Institute of Environment, University of Tabriz, Tabriz, East Azerbaijan, Iran; Traditional Medicine and Hydrotherapy Research Center, Ardabil University of Medical Sciences, Ardabil, Iran.
| | - Mojgan Bordbar
- University of Campania "Luigi Vanvitelli", Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Caserta, Italy
| | - Mohammad Reza Nikoo
- Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.
| | - Leila Sadat Seyyed Silabi
- Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran.
| | | | - Yong Xiao
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China.
| |
Collapse
|
16
|
Guo X, Xiong H, Li H, Gui X, Hu X, Li Y, Cui H, Qiu Y, Zhang F, Ma C. Designing dynamic groundwater management strategies through a composite groundwater vulnerability model: Integrating human-related parameters into the DRASTIC model using LightGBM regression and SHAP analysis. ENVIRONMENTAL RESEARCH 2023; 236:116871. [PMID: 37573023 DOI: 10.1016/j.envres.2023.116871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/20/2023] [Accepted: 08/09/2023] [Indexed: 08/14/2023]
Abstract
Groundwater nitrate contamination has emerged as a pressing global concern. Given its potential for long-term impacts on aquifers, protective measures should primarily focus on prevention. Drawing on the theory of groundwater vulnerability (GV), the original DRASTIC model and parameters related to human activities are employed as inputs and integrated with the LightGBM regression algorithm to facilitate nitrate index (NI) prediction tasks. The SHAP analysis is conducted to effectively examine the contribution of parameters to the NI prediction and interpret the issue of parameter interactions. In addition, to mitigate the limitations of the intrinsic GV model, a composite nitrate index (CNI) is developed by linearly combining the DRASTIC index with the NI. The framework presented in this study provides adaptive strategies for managing groundwater resources over different time periods. A representative region for arid and semiarid climates, the Yinchuan region, is studied using the framework. As compared to 2012, the intrinsic GV index has changed spatially in 2022. Human activities have increased the influence of the nitrate concentration as shown by the Pearson correlation coefficient of -0.082 between the DRASTIC index and nitrate concentration. A significant increase in pollution levels was predicted by NI, ranging from -0.116 to 0.968. According to SHAP analysis, the significant increase in NI levels in 2022 was mainly due to high-value industrial and agricultural production. In 2022, 12.02% of the areas had an increase of at least 0.549 in the CNI. 42.1% of the areas were classified as moderate or high CNI levels. The farm was identified as a high-contributing source to nitrate pollution. The small-scale agricultural and livestock activities in non-urban areas also contribute to groundwater pollution. Dynamic groundwater management strategies need to be implemented in high-growth and high-level CNI areas.
Collapse
Affiliation(s)
- Xu Guo
- School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China.
| | - Hanxiang Xiong
- School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China
| | - Haixue Li
- School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China; Center for Hydrogeology and Environmental Geology Survey, China Geological Survey, Baoding, 071051, Hebei, China
| | | | - Xiaojing Hu
- School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China
| | - Yonggang Li
- School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China
| | - Hao Cui
- School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China
| | - Yang Qiu
- School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China
| | - Fawang Zhang
- School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China; Center for Hydrogeology and Environmental Geology Survey, China Geological Survey, Baoding, 071051, Hebei, China.
| | - Chuanming Ma
- School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China.
| |
Collapse
|
17
|
Haggerty R, Sun J, Yu H, Li Y. Application of machine learning in groundwater quality modeling - A comprehensive review. WATER RESEARCH 2023; 233:119745. [PMID: 36812816 DOI: 10.1016/j.watres.2023.119745] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 11/30/2022] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The prediction of groundwater pollution due to various chemical components is vital for planning, policymaking, and management of groundwater resources. In the last two decades, the application of machine learning (ML) techniques for groundwater quality (GWQ) modeling has grown exponentially. This review assesses all supervised, semi-supervised, unsupervised, and ensemble ML models implemented to predict any groundwater quality parameter, making this the most extensive modern review on this topic. Neural networks are the most used ML model in GWQ modeling. Their usage has declined in recent years, giving rise to more accurate or advanced techniques such as deep learning or unsupervised algorithms. Iran and the United States lead the world in areas modeled, with a wealth of historical data available. Nitrate has been modeled most exhaustively, targeted by nearly half of all studies. Advancements in future work will be made with further implementation of deep learning and explainable artificial intelligence or other cutting-edge techniques, application of these techniques for sparsely studied variables, the modeling of new or unique study areas, and the implementation of ML techniques for groundwater quality management.
Collapse
Affiliation(s)
- Ryan Haggerty
- Department of Civil and Environmental Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
| | - Jianxin Sun
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
| | - Hongfeng Yu
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, United States; Holland Computing Center, University of Nebraska-Lincoln, Lincoln, NE 68588, United States
| | - Yusong Li
- Department of Civil and Environmental Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, United States.
| |
Collapse
|
18
|
Elzain HE, Chung SY, Venkatramanan S, Selvam S, Ahemd HA, Seo YK, Bhuyan MS, Yassin MA. Novel machine learning algorithms to predict the groundwater vulnerability index to nitrate pollution at two levels of modeling. CHEMOSPHERE 2023; 314:137671. [PMID: 36586442 DOI: 10.1016/j.chemosphere.2022.137671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 12/12/2022] [Accepted: 12/24/2022] [Indexed: 06/17/2023]
Abstract
The accurate mapping and assessment of groundwater vulnerability index are crucial for the preservation of groundwater resources from the possible contamination. In this research, novel intelligent predictive Machine Learning (ML) regression models of k-Neighborhood (KNN), ensemble Extremely Randomized Trees (ERT), and ensemble Bagging regression (BA) at two levels of modeling were utilized to improve DRASTIC-LU model in the Miryang aquifer located in South Korea. The predicted outputs from level 1 (KNN and ERT models) were used as inputs for ensemble bagging (BA) in level 2. The predictive groundwater pollution vulnerability index (GPVI), derived from DRASTIC-LU model was adjusted by NO3-N data and was utilized as the target data of the ML models. Hyperparameters for all models were tuned using a Grid Searching approach to determine the best effective model structures. Various statistical metrics and graphical representations were used to evaluate the superior predictive performance among ML models. Ensemble BA model in level 2 was more precise than standalone KNN and ensemble ERT models in level 1 for predicting GPVI values. Furthermore, the ensemble BA model offered suitable outcomes for the unseen data that could subsequently prevent the overfitting issue in the testing phase. Therefore, ML modeling at two levels could be an excellent approach for the proactive management of groundwater resources against contamination.
Collapse
Affiliation(s)
- Hussam Eldin Elzain
- Department. of Environmental & Earth Sciences, Pukyong National University, Busan, 48513, South Korea; Water Research Center, Sultan Qaboos University, Muscat, Oman.
| | - Sang Yong Chung
- Department. of Environmental & Earth Sciences, Pukyong National University, Busan, 48513, South Korea.
| | - Senapathi Venkatramanan
- Department of Disaster Management, Alagappa University, Karaikudi, Tamil Nadu, 630003, India.
| | - Sekar Selvam
- Department of Geology, V. O. Chidambaram College, Tuticorin, Tamil Nadu, 628008, India.
| | - Hamdi Abdurhman Ahemd
- Department of Industrial and Data Engineering, Pukyong National University, Busan, 48513, South Korea.
| | - Young Kyo Seo
- Geo-Marine Technology (GEMATEK), Busan, 48071, South Korea.
| | - Md Simul Bhuyan
- Bangladesh Oceanographic Research Institute, Cox's Bazar -4730, Bangladesh.
| | - Mohamed A Yassin
- Interdisciplinary Research Center for Membranes and Water Security, KFUPM, 31261, Saudi Arabia.
| |
Collapse
|
19
|
Taghavi N, Niven RK, Kramer M, Paull DJ. Comparison of DRASTIC and DRASTICL groundwater vulnerability assessments of the Burdekin Basin, Queensland, Australia. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 858:159945. [PMID: 36343801 DOI: 10.1016/j.scitotenv.2022.159945] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/23/2022] [Accepted: 10/31/2022] [Indexed: 06/16/2023]
Abstract
In the Burdekin Basin, Queensland, Australia, groundwater contamination due to agricultural activities has led to concerns over its impacts on globally significant ecosystems such as the Great Barrier Reef. An appropriate method for groundwater vulnerability assessment is essential for the sustainable use of this groundwater resource and its longer-term environmental management. The aim of this study is to apply and assess the suitability of the standard DRASTIC index-based method for groundwater vulnerability assessment of the Burdekin Basin. The intrinsic groundwater vulnerability is calculated in ArcGIS, using data for the period 2010 to 2021. The results are compared to available water quality data. The calculated DRASTIC scores are found to be only weakly correlated with water quality parameters, including the nitrate concentration (R = 0.07), which should behave as a proxy measure of groundwater vulnerability. To address this, a modified DRASTICL method containing a land use parameter is also implemented, to assess the specific groundwater vulnerability. The correlation between DRASTICL scores and nitrate levels (R = 0.2) is more significant but is still relatively weak. From this study, it is recommended that alternative methods be developed to assess groundwater vulnerability in the Burdekin Basin, and other comparable aquifer systems.
Collapse
Affiliation(s)
- Nasrin Taghavi
- School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT 2600, Australia
| | - Robert K Niven
- School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT 2600, Australia.
| | - Matthias Kramer
- School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT 2600, Australia
| | - David J Paull
- School of Science, The University of New South Wales, Canberra, ACT 2600, Australia
| |
Collapse
|
20
|
Saha A, Pal SC, Chowdhuri I, Roy P, Chakrabortty R. Effect of hydrogeochemical behavior on groundwater resources in Holocene aquifers of moribund Ganges Delta, India: Infusing data-driven algorithms. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 314:120203. [PMID: 36150620 DOI: 10.1016/j.envpol.2022.120203] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 08/16/2022] [Accepted: 09/15/2022] [Indexed: 06/16/2023]
Abstract
One of the fundamental sustainable development goals has been recognized as having access to clean water for drinking purposes. In the Anthropocene era, rapid urbanization put further stress on water resources, and associated groundwater contamination expanded into a significant global environmental issue. Natural arsenic and related water pollution have already caused a burden issue on groundwater vulnerability and corresponding health hazard in and around the Ganges delta. A field based hydrogeochemical analysis has been carried out in the elevated arsenic prone areas of moribund Ganges delta, West Bengal, a part of western Ganga- Brahmaputra delta (GBD). New data driven heuristic algorithms are rarely used in groundwater vulnerability studies, specifically not yet used in the elevated arsenic prone areas of Ganges delta, India. Therefore, in the current study, emphasis has been given on integration of heuristic algorithms and random forest (RF) i.e., "RF-particle swarm optimization (PSO)", "RF-grey wolf optimizer (GWO)" and "RF-grasshopper optimization algorithm (GOA)", to identify groundwater vulnerable zones on the basis of field based hydrogeochemical parameters. In addition, correspondence health hazard of this area was assessed through human health hazard index. The spatial distribution of groundwater vulnerability revealed that middle-eastern and north-western part of the study area covered by very high and high, whereas central, western and south-western part are covered by very low and low vulnerability zones in outcomes of all the applied models. The evaluation result indicates that RF-GOA (AUC = 0.911) model performed the best considering testing dataset, and thereafter RF-GWO, RF-PSO and RF with AUC value is 0.901, 0.892 and 0.812 respectively. Findings also revealed the groundwater in this study region is quite unfavorable for drinking and irrigation purposes. The suggested models demonstrate their usefulness in foretelling sustainable groundwater resource management in various deltaic regions of the world through taking appropriate measures by policy-makers.
Collapse
Affiliation(s)
- Asish Saha
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal, 713104, India
| | - Subodh Chandra Pal
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal, 713104, India.
| | - Indrajit Chowdhuri
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal, 713104, India
| | - Paramita Roy
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal, 713104, India
| | - Rabin Chakrabortty
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal, 713104, India
| |
Collapse
|
21
|
Boudabra B, Agoubi B. A groundwater risk assessment for irrigation purpose based on salinity indicators: applied to southeastern Tunisia. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 195:38. [PMID: 36301378 DOI: 10.1007/s10661-022-10613-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
Groundwater resources are increasingly under demand to support human activities and economic development. This naturally clean resource is threatened by human and natural pollutions. Protecting groundwater has become a challenge for both urban and rural communities. This work aims to develop a simple and effective approach to assess the risk of aquifer pollution and the delineation of risk zones. To do so, an aquifer risk index (ARI) was presented as an overall indicator of the risk of groundwater pollution. The ARI is calculated using the groundwater vulnerability index (GVI) and the normalized water quality index (WQI). The multi-linear regression (MLR) model was used to assign weights to each parameter. This model has been applied and validated in the Koutine area of southern Tunisia. The results revealed that the groundwater in the study area is characterized by low and moderate risk. Fifty-six percent (56%) of the total study area presents a low risk. The remaining 44% of the study area is classified as medium risk, primarily in the Wadi and Lowland plains. ARI considers the intrinsic characteristics of the aquifer and groundwater quality when assessing the risk of pollution. The approach developed for assessing groundwater risks is simple to implement, realistic, and efficient. AVI is a parametric approach that uses aquifer data without losing information. This method can assist academics and groundwater resource managers in delineating risk areas to protect groundwater resources from pollution.
Collapse
Affiliation(s)
- Belgacem Boudabra
- Higher Institute of Water Sciences and Techniques, University of Gabes, Gabes, Tunisia
- RL: Applied HydroSciences Laboratory Research, Gabes, Tunisia
| | - Belgacem Agoubi
- Higher Institute of Water Sciences and Techniques, University of Gabes, Gabes, Tunisia.
- RL: Applied HydroSciences Laboratory Research, Gabes, Tunisia.
| |
Collapse
|
22
|
Atoui M, Agoubi B. Assessment of groundwater vulnerability and pollution risk using AVI, SPI, and RGPI indexes: applied to southern Gabes aquifer system, Tunisia. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:50881-50894. [PMID: 35239122 DOI: 10.1007/s11356-022-19309-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/15/2022] [Indexed: 06/14/2023]
Abstract
Southern Gabes aquifer is part of coastal Jeffara plain located in southeastern Tunisia. It consists a semi-arid area in which groundwater is the main source to water supply for several socio-economic sectors. Southern Gabes aquifer suffers from excessive abstraction and heavy anthropogenic pressures that make local groundwater resources threatened by pollution risks. This study aims to assess groundwater vulnerability, evaluate, and delineate groundwater risk regions. For this, a 17 water samples were carried out in the study area and chemical compositions were analyzed. A well-known AVI model has been used to assess aquifer vulnerability and new algorithms of sensitivity to pollution index (PSI) and risk groundwater to pollution index (RGPI) were implemented and used to assess, classify, and map groundwater pollution risk. Results reveal that study area suffers from high risk. Forty one percent of the total surface of study area has a very high risk. Nonetheless, only 30% of study area has a low to insignificant risk to pollution which necessitates taking severe precautions to protect the southern Gabes aquifer system. The method used in this study seems giving more precise results compared to conventional approaches. Moreover, this method allows assessing the pollution risk with flexible and reliable algorithm even with limited dataset. Hence, the poor natural protective capacity of study area needs a rapid intervention by local authorities in order to develop proactive solutions to protect and preserve groundwater resources from pollution risks and establish a long-term program for groundwater resources sustainable development.
Collapse
Affiliation(s)
- Mounir Atoui
- Higher Institute of Water Sciences and Techniques, University of Gabes, Gabes, Tunisia
- Applied Hydro-Sciences Research Laboratory, Gabes, Tunisia
| | - Belgacem Agoubi
- Higher Institute of Water Sciences and Techniques, University of Gabes, Gabes, Tunisia.
- Applied Hydro-Sciences Research Laboratory, Gabes, Tunisia.
| |
Collapse
|
23
|
Abstract
The scope of the present study is the estimation of the concentration of nitrates (NO3−) in groundwater using artificial neural networks (ANNs) based on easily measurable in situ data. For the purpose of the current study, two feedforward neural networks were developed to determine whether including land use variables would improve the model results. In the first network, easily measurable field data were used, i.e., pH, electrical conductivity, water temperature, air temperature, and aquifer level. This model achieved a fairly good simulation based on the root mean squared error (RMSE in mg/L) and the Nash–Sutcliffe Model Efficiency (NSE) indicators (RMSE = 26.18, NSE = 0.54). In the second model, the percentages of different land uses in a radius of 1000 m from each well was included in an attempt to obtain a better description of nitrate transport in the aquifer system. When these variables were used, the performance of the model increased significantly (RMSE = 15.95, NSE = 0.70). For the development of the models, data from chemical and physical analyses of groundwater samples from wells located in the Kopaidian Plain and the wider area of the Asopos River Basin, both in Greece, were used. The simulation that the models achieved indicates that they are a potentially useful tools for the estimation of groundwater contamination by nitrates and may therefore constitute a basis for the development of groundwater management plans.
Collapse
|