Integration of Watershed eco-physical health through Algorithmic game theory and supervised machine learning

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 26, P. 101216 - 101216

Published: May 31, 2024

The eco-physical health assessment of watersheds is crucial for sustainable water resource management and ecosystem services. This study quantifies the Talar watershed in Iran using geometric mean method (GMM), game-theoretic algorithm (GTA), machine learning algorithms including Random Forest (RF), Support Vector Machine (SVM), Simple Linear Regression (SLR), K-Nearest Neighbor (KNN) distributed semi-distributed monitoring. results show that RF performed better than other models, as indicated by MAE, MSE, RMSE, AUC statistics with values 0.032, 0.003, 0.058, 0.940, respectively. index prioritization different approaches showed pattern changes positively from upstream to downstream. Based on GMM, it can be said sub-watersheds Int6 Int5 are healthiest studied watershed, 0.93 0.90, GTA approach, also Int6, Int5, Int01 ones. In case algorithm, average pixels each sub-watershed were recognized 0.91 0.88, consistently emerged across all methods, attributed high TWI NDVI low slope, DEM, erosion, CN values. general, catchment fully followed factors affecting catchment's spatial patterns change this consistent physiographic hydroclimatic conditions three approaches. study's implications underline importance multi-criteria multi-algorithm accurately assessing managing development.

Language: Английский

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Groundwater quality assessment using machine learning models: a comprehensive study on the industrial corridor of a semi-arid region

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: unknown

Published: July 4, 2024

Language: Английский

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Machine Learning-based Model for Groundwater Quality Prediction: A Comprehensive Review and Future Time–Cost Effective Modelling Vision

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 19, 2025

Language: Английский

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Machine learning modeling of base flow generation potential: A case study of the combined application of BWM and Fallback bargaining algorithm

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 636, P. 131220 - 131220

Published: April 19, 2024

Language: Английский

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Conjunct applicability of MCDM-based machine learning algorithms in mapping the sediment formation potential

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 13, 2024

Abstract This study evaluates the applicability of multicriteria decision-making (MCDM) methods, including SAW, VIKOR, TOPSIS, and Condorcet algorithm based on game theory machine learning algorithms (MLAs) K-nearest neighbor, Naïve Bayes, Random Forest (RF), simple linear regression support vector in spatial mapping sediment formation potential Talar watershed, Iran. In first approach, MCDM was used, Condorcet’s theory. To this end, a decision matrix for created factors affecting potential. next step, various MLAs were used to construct distribution map Finally, constructed very low high classes. The summary results prioritizing sub-basins using multi-criteria methods showed that sub-basin SW12 had highest methods. modeling different values error statistics, RF with MAE = 0.032, MSE 0.024, RMSE 0.155, AUC 0.930 selected as most optimal algorithm. On other side, correlation Taylor diagram (Figs. 10 11) also slope factor value 0.84. Also, LS coefficient 0.65 after model modeling. shows amount increases from downstream upstream side watershed.

Language: Английский

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Changes in the characteristics of water quality parameters under the influence of dam construction

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: May 8, 2024

Language: Английский

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Evaluation of Groundwater Quality Through Identification of Potential Contaminant

Civil And Environmental Engineering Reports, Journal Year: 2024, Volume and Issue: 34(4), P. 185 - 206

Published: Oct. 29, 2024

Groundwater, is crucial for human consumption and industrial purposes, demands continuous monitoring to assess quality standards. This study conducts a comprehensive evaluation of groundwater its overall condition identify potential contaminants. The research predicts the presence levels contaminants such as heavy metals, organic pollutants, microbial agents using hydrogeological studies, chemical analysis, statistical modelling. A covariance analysis identified places with low water quality. Analysis shows most samples satisfy drinking requirements. consolidated map illustrates significant expanse suitable domestic particularly in terms However, 2467.09 sq. km deemed unacceptable. Further including correlation, ANOVA, t-tests One Sample Test, Bayesian Statistics, Power Analysis, identifies 836.87 under category maximum permissible 9.19 highly desirable use.

Language: Английский

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Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(40), P. 52740 - 52757

Published: Aug. 19, 2024

Language: Английский

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Utilizing InVEST ecosystem services model combined with deep learning and fallback bargaining for effective sediment retention in Northern Iran

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 14, 2024

Language: Английский

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