A comparative hydrochemical assessment of groundwater quality for drinking and irrigation purposes using different statistical and ML models in lower gangetic alluvial plain, eastern India

Chemosphere, Journal Year: 2025, Volume and Issue: 372, P. 144074 - 144074

Published: Jan. 13, 2025

Language: Английский

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Deciphering Effects of Coal Fly Ash on Hydrochemistry and Heavy Metal(loid)s Occurrence in Surface and Groundwater: Implications for Environmental Impacts and Management

Water Air & Soil Pollution, Journal Year: 2024, Volume and Issue: 235(10)

Published: Aug. 28, 2024

Language: Английский

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Assessment of the Groundwater Quality of the Jessore Municipality by Using Weighted Arithmetic Water Quality Index

Next research., Journal Year: 2025, Volume and Issue: 2(1), P. 100187 - 100187

Published: Feb. 3, 2025

Language: Английский

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An Optimized Approach for Predicting Water Quality Features and A Performance evaluation for Mapping Surface Water Potential Zones Based on Discriminant Analysis (DA), Geographical Information System (GIS) and Machine Learning (ML) Models in Baitarani River Basin, Odisha

Desalination and Water Treatment, Journal Year: 2025, Volume and Issue: 321, P. 101039 - 101039

Published: Jan. 1, 2025

Language: Английский

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Future Research Imperatives in Hydrogeology

Springer hydrogeology, Journal Year: 2025, Volume and Issue: unknown, P. 365 - 385

Published: Jan. 1, 2025

Language: Английский

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Machine-Learning Models and Global Sensitivity Analyses to Explicitly Estimate Groundwater Presence Validated by Observed Dataset at K-NET in Japan

Geosciences, Journal Year: 2025, Volume and Issue: 15(4), P. 126 - 126

Published: April 1, 2025

This study incorporates the comprehensively observed proxies of in situ geotechnical, geophysical, petrophysical, and lithological datasets to estimate groundwater presence. Two machine-learning approaches, random forest regression (RFR) deep neural network (DNN), are applied. The constructed RFR DNN models validated using depths levels at 772 K-NET sites Japan. model exhibited effectiveness robust performance compared poor-fitting previous detection physical-based approaches. yielded a remarkable 1:1 agreement between predicted 733 470 sites, respectively. During training process, all were split into training, validating, unseen testing with ratio set 1:1:11. k-fold cross-validation strategy demonstrates better-fitting for model. contributions interactions among utilizing variance-based global sensitivity analyses can be understood. P-wave velocity standard penetration test values have prominent other depths. To apply any given site, reliable detailed P- S-wave structures crucial building needed source datasets.

Language: Английский

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Geospatial Techniques for the Delineation of Surface Water Potential Zones and Advanced Optimization Approaches for Improving Water Quality Assessment in the Mahanadi River Basin, Odisha, India

Published: Jan. 1, 2025

Language: Английский

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Optimized intelligent learning for groundwater quality prediction in diverse aquifers of arid and semi-arid regions of India

Cleaner Engineering and Technology, Journal Year: 2025, Volume and Issue: 26, P. 100984 - 100984

Published: May 1, 2025

Language: Английский

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Surface water quality evaluation, apportionment of pollution sources and aptness testing for drinking using water quality indices and multivariate modelling in Baitarani River basin, Odisha

HydroResearch, Journal Year: 2024, Volume and Issue: 8, P. 244 - 264

Published: Dec. 10, 2024

Language: Английский

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Machine Learning-driven Optimization of Water Quality Index: A Synergistic ENTROPY-CRITIC Approach Using Spatio-Temporal Data

Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 8(4), P. 1453 - 1475

Published: Oct. 24, 2024

Language: Английский

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