A comparative assessment of flood susceptibility modelling of GIS-based TOPSIS, VIKOR, and EDAS techniques in the Sub-Himalayan foothills region of Eastern India

Environmental Science and Pollution Research, Год журнала: 2022, Номер 30(6), С. 16036 - 16067

Опубликована: Сен. 30, 2022

Язык: Английский

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Coupling Machine and Deep Learning with Explainable Artificial Intelligence for Improving Prediction of Groundwater Quality and Decision-Making in Arid Region, Saudi Arabia

Water, Год журнала: 2023, Номер 15(12), С. 2298 - 2298

Опубликована: Июнь 20, 2023

Recently, machine learning (ML) and deep (DL) models based on artificial intelligence (AI) have emerged as fast reliable tools for predicting water quality index (WQI) in various regions worldwide. In this study, we propose a novel stacking framework DL WQI prediction, employing convolutional neural network (CNN) model. Additionally, introduce explainable AI (XAI) through XGBoost-based SHAP (SHapley Additive exPlanations) values to gain valuable insights that can enhance decision-making strategies management. Our findings demonstrate the model achieves highest accuracy prediction (R2: 0.99, MAPE: 15.99%), outperforming CNN 0.90, 58.97%). Although shows relatively high R2 value, other statistical measures indicate it is actually worst-performing among five tested. This discrepancy may be attributed limited training data available Furthermore, application of techniques, specifically values, allows us into extract information management purposes. The interaction plot reveal elevated levels total dissolved solids (TDS), zinc, electrical conductivity (EC) are primary drivers poor quality. These parameters exhibit nonlinear relationship with index, implying even minor increases their concentrations significantly impact Overall, study presents comprehensive integrated approach management, emphasizing need collaborative efforts all stakeholders mitigate pollution uphold By leveraging XAI, our proposed not only provides powerful tool accurate but also offers models, enabling informed strategies.

Язык: Английский

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Empowered machine learning algorithm to identify sustainable groundwater potential zone map in Jashore District, Bangladesh

Groundwater for Sustainable Development, Год журнала: 2024, Номер 25, С. 101168 - 101168

Опубликована: Апрель 5, 2024

Язык: Английский

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Sustainable groundwater management in coastal cities: Insights from groundwater potential and vulnerability using ensemble learning and knowledge-driven models

Journal of Cleaner Production, Год журнала: 2024, Номер 442, С. 141152 - 141152

Опубликована: Фев. 1, 2024

Язык: Английский

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Sensitivity analysis-driven machine learning approach for groundwater quality prediction: Insights from integrating ENTROPY and CRITIC methods

Groundwater for Sustainable Development, Год журнала: 2024, Номер 26, С. 101309 - 101309

Опубликована: Авг. 1, 2024

Язык: Английский

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Forest fire probability zonation using dNBR and machine learning models: a case study at the Similipal Biosphere Reserve (SBR), Odisha, India

Environmental Science and Pollution Research, Год журнала: 2025, Номер unknown

Опубликована: Янв. 30, 2025

Язык: Английский

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Groundwater pollution and its remediation for sustainable water management

Chemosphere, Год журнала: 2023, Номер 329, С. 138621 - 138621

Опубликована: Апрель 7, 2023

Язык: Английский

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Conjunct application of machine learning and game theory in groundwater quality mapping

Environmental Earth Sciences, Год журнала: 2023, Номер 82(17)

Опубликована: Авг. 9, 2023

Abstract Groundwater quality (GWQ) monitoring is one of the best environmental objectives due to recent droughts and urban rural development. Therefore, this study aimed map GWQ in central plateau Iran by validating machine learning algorithms (MLAs) using game theory (GT). On basis, chemical parameters related water quality, including K + , Na Mg 2+ Ca SO 4 2− Cl − HCO 3 pH, TDS, EC, were interpolated at 39 sampling sites. Then, random forest (RF), support vector (SVM), Naive Bayes, K-nearest neighbors (KNN) used Python programming language, was plotted concerning GWQ. Borda scoring validate MLAs, sample points prioritized. Based on results, among ML algorithms, RF algorithm with error statistics MAE = 0.261, MSE 0.111, RMSE 0.333, AUC 0.930 selected as most optimal algorithm. created algorithm, 42.71% studied area poor condition. The proportion region classes moderate high 18.93% 38.36%, respectively. results prioritization sites GT showed a great similarity between model. In addition, analysis condition critical non-critical based that aspects, carbonate balance, salinity general, it can be said simultaneous use MLA provides good basis for constructing Iran.

Язык: Английский

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Multi-criteria decision-making for groundwater potentiality zonation in a groundwater scarce region in central India using methods of compensatory aggregating functions

Groundwater for Sustainable Development, Год журнала: 2024, Номер 25, С. 101101 - 101101

Опубликована: Фев. 2, 2024

Язык: Английский

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Application of bagging and boosting ensemble machine learning techniques for groundwater potential mapping in a drought-prone agriculture region of eastern India

Environmental Sciences Europe, Год журнала: 2024, Номер 36(1)

Опубликована: Сен. 2, 2024

Groundwater is a primary source of drinking water for billions worldwide. It plays crucial role in irrigation, domestic, and industrial uses, significantly contributes to drought resilience various regions. However, excessive groundwater discharge has left many areas vulnerable potable shortages. Therefore, assessing potential zones (GWPZ) essential implementing sustainable management practices ensure the availability present future generations. This study aims delineate with high Bankura district West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient (XGBoost), Voting Ensemble (VE). The models used 161 data points, comprising 70% training dataset, identify significant correlations between presence absence region. Among methods, (RF) (XGBoost) proved be most effective mapping potential, suggesting their applicability other regions similar hydrogeological conditions. performance metrics RF are very good precision 0.919, recall 0.971, F1-score 0.944, accuracy 0.943. indicates strong capability accurately predict minimal false positives negatives. (AdaBoost) demonstrated comparable across all (precision: recall: F1-score: accuracy: 0.943), highlighting its effectiveness predicting accurately; whereas, outperformed slightly, higher values metrics: (0.944), (0.971), (0.958), (0.957), more refined model performance. (VE) approach also showed enhanced performance, mirroring XGBoost's 0.958, 0.957). that combining strengths individual leads better predictions. potentiality zoning varied significantly, low accounting 41.81% at 24.35%. uncertainty predictions ranged from 0.0 0.75 area, reflecting variability need targeted strategies. In summary, this highlights critical managing resources effectively advanced techniques. findings provide foundation practices, ensuring use conservation beyond.

Язык: Английский

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