Comparative study of machine learning models for evaluating groundwater vulnerability to nitrate contamination

Ecotoxicology and Environmental Safety, Год журнала: 2021, Номер 229, С. 113061 - 113061

Опубликована: Дек. 11, 2021

The accurate evaluation of groundwater contamination vulnerability is essential for the management and prevention in watershed. In this study, advanced multiple machine learning (ML) models Radial Basis Neural Networks (RBNN), Support Vector Regression (SVR), ensemble Random Forest (RFR) were applied to determine most performance vulnerability. Eight factors DRASTIC-L rated based on modified DRASTIC model (MDM) used as input data. adjusted index (AVI) with nitrate values was output data modeling process. three verified using statistical criteria MAE, RMSE, r2, ROC/AUC values. RFR showed highest comparison standalone SVR RBNN models. Specifically, kept all promising solutions during due its flexibility robustness, map obtained by more predicting vulnerable areas contamination. It concluded that a robust tool enhance vulnerability, it could contribute environmental safety against

Язык: Английский

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Integrated variable weight model and improved DRASTIC model for groundwater vulnerability assessment in a shallow porous aquifer

Journal of Hydrology, Год журнала: 2022, Номер 608, С. 127538 - 127538

Опубликована: Янв. 29, 2022

Язык: Английский

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Hydrogeochemical evaluation and corresponding health risk from elevated arsenic and fluoride contamination in recurrent coastal multi-aquifers of eastern India

Journal of Cleaner Production, Год журнала: 2022, Номер 369, С. 133150 - 133150

Опубликована: Июль 31, 2022

Язык: Английский

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Water pollution in India – Current scenario

Water Security, Год журнала: 2022, Номер 16, С. 100119 - 100119

Опубликована: Апрель 11, 2022

Язык: Английский

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Groundwater vulnerability and contamination risk mapping of semi-arid Totko river basin, India using GIS-based DRASTIC model and AHP techniques

Chemosphere, Год журнала: 2022, Номер 307, С. 135831 - 135831

Опубликована: Авг. 6, 2022

Язык: Английский

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Machine Learning Algorithms for Modeling and Mapping of Groundwater Pollution Risk: A Study to Reach Water Security and Sustainable Development (Sdg) Goals in a Mediterranean Aquifer System

Remote Sensing, Год журнала: 2022, Номер 14(10), С. 2379 - 2379

Опубликована: Май 15, 2022

Groundwater pollution poses a severe threat and issue to the environment humanity overall. That is why mitigative strategies are urgently needed. Today, studies mapping groundwater risk assessment being developed. In this study, five new hybrid/ensemble machine learning (ML) models developed, named DRASTIC-Random Forest (RF), DRASTIC-Support Vector Machine (SVM), DRASTIC-Multilayer Perceptron (MLP), DRASTIC-RF-SVM, DRASTIC-RF-MLP, for in Saiss basin, Morocco. The performances of these evaluated using Receiver Operating Characteristic curve (ROC curve), precision, accuracy. Based on results ROC method, it indicated that use improves performance individual algorithms. effect, AUC value original DRASTIC 0.51. Furthermore, both models, DRASTIC-RF-MLP (AUC = 0.953) 0.901) achieve best accuracy among other followed by DRASTIC-RF 0.852), DRASTIC-SVM 0.802), DRASTIC-MLP 0.763). delineate areas vulnerable pollution, which require urgent actions improve environmental social qualities local population.

Язык: Английский

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Spatial evaluation of groundwater vulnerability using the DRASTIC-L model with the analytic hierarchy process (AHP) and GIS approaches in Edo State, Nigeria

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2024, Номер 134, С. 103562 - 103562

Опубликована: Янв. 21, 2024

Язык: Английский

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Enhancing groundwater vulnerability assessment for improved environmental management: addressing a critical environmental concern

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(13), С. 19185 - 19205

Опубликована: Фев. 15, 2024

Groundwater serves as a primary water source for various purposes. Therefore, aquifer pollution poses critical threat to human health and the environment. Identifying aquifer's highly vulnerable areas is necessary implement appropriate remedial measures, thus ensuring groundwater sustainability. This paper aims enhance vulnerability assessment (GWVA) manage quality effectively. The study focuses on El Orjane Aquifer in Moulouya basin, Morocco, which facing significant degradation due olive mill wastewater. maps (GVMs) were generated using DRASTIC, Pesticide SINTACS, SI methods. To assess effectiveness of proposed improvements, 24 piezometers installed measure nitrate concentrations, common indicator contamination. aimed GWVA by incorporating new layers, such land use, adjusting parameter rates based comprehensive sensitivity analysis. results demonstrate increase Pearson correlation values (PCV) between produced GVMs measured concentrations. For instance, PCV DRASTIC method improved from 0.42 0.75 after adding use layer Wilcoxon method. These findings offer valuable insights accurately assessing with similar hazards hydrological conditions, particularly semi-arid arid regions. They contribute improving environmental management practices, long-term sustainability aquifers.

Язык: Английский

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Understanding the suitability of two MCDM techniques in mapping the groundwater potential zones of semi-arid Bankura District in eastern India

Groundwater for Sustainable Development, Год журнала: 2022, Номер 17, С. 100727 - 100727

Опубликована: Фев. 14, 2022

Язык: Английский

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Predicting Regional-Scale Elevated Groundwater Nitrate Contamination Risk Using Machine Learning on Natural and Human-Induced Factors

ACS ES&T Engineering, Год журнала: 2022, Номер 2(4), С. 689 - 702

Опубликована: Март 16, 2022

Elevated groundwater nitrate poses risk to the ecosystem and human health, delineating extent of elevated is essential for effective management public health safety. Here, using machine learning models (Random Forest, Boosted Regression Tree, Logistic Regression) on a large, in situ dataset, we have predicted first nationwide contamination (concentration >45 mg/L) across India. We also aimed delineate intrinsic (e.g., climate, geomorphic, hydrogeologic) extraneous anthropogenic input) predictors identifying pollution risk. Of these models, Random Forest performed best was considered develop final prediction map at 1 km2 resolution. Climate variables like precipitation aridity, influence, e.g., fertilizer application population density, were identified as most important predicting Dry arid semiarid regions west, south, central parts country contained majority high-risk areas. Predictions suggested that about 37% India's areal 380 million people exposed nitrate. The model satisfactorily over validation dataset indicates ability local scale. study aims provide an approach aid development awareness strategies uphold

Язык: Английский

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