Integration of hydrogeological data, GIS and AHP techniques applied to delineate groundwater potential zones in sandstone, limestone and shales rocks of the Damoh district, (MP) central India

Environmental Research, Journal Year: 2023, Volume and Issue: 228, P. 115832 - 115832

Published: April 11, 2023

Language: Английский

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Critical role of climate factors for groundwater potential mapping in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 621, P. 129599 - 129599

Published: May 1, 2023

Language: Английский

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Heavy metals concentration in soils across the conterminous USA: Spatial prediction, model uncertainty, and influencing factors

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 919, P. 170972 - 170972

Published: Feb. 13, 2024

Assessment and proper management of sites contaminated with heavy metals require precise information on the spatial distribution these metals. This study aimed to predict map Cd, Cu, Ni, Pb, Zn across conterminous USA using point observations, environmental variables, Histogram-based Gradient Boosting (HGB) modeling. Over 9180 surficial soil observations from Soil Geochemistry Spatial Database (SGSD) (n = 1150), Geochemical Mineralogical Survey Soils (GMSS) 4857), Holmgren Dataset (HD) 3400), 28 covariates (100 m × 100 grid) representing climate, topography, vegetation, soils, anthropic activity were compiled. Model performance was evaluated 20 % data not used in calibration coefficient determination (R2), concordance correlation (ρc), root mean square error (RMSE) indices. Uncertainty predictions calculated as difference between estimated 95 5 quantiles provided by HGB. The model explained up 50 variance RMSE ranging 0.16 (mg kg−1) for Cu 23.4 Zn, respectively. Likewise, ρc ranged 0.55 (Cu) 0.68 (Zn), respectively, had highest R2 (0.50) among all predictions. We observed high Pb concentrations near urban areas. Peak studied found Lower Mississippi River Valley. higher West Coast; Cd central USA. Clay, pH, potential evapotranspiration, temperature, precipitation model's top five important combined use coupled machine learning a reliable prediction soils updated maps could support assessments, monitoring, decision-making this methodology applicable other databases, worldwide.

Language: Английский

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Suitability and its Physicochemical Characterization for Deciphering Surface Water Quality Using Entropy (E) and Fuzzy (F)-AHP Optimization Model in Mahanadi River Basin (MRB), Odisha (India)

Water science and technology library, Journal Year: 2025, Volume and Issue: unknown, P. 457 - 497

Published: Jan. 1, 2025

Language: Английский

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Machine-learning modelling of fire susceptibility in a forest-agriculture mosaic landscape of southern India

Ecological Informatics, Journal Year: 2021, Volume and Issue: 64, P. 101348 - 101348

Published: June 9, 2021

Language: Английский

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A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications

Water, Journal Year: 2022, Volume and Issue: 14(8), P. 1230 - 1230

Published: April 11, 2022

This review focuses on the use of Interpretable Artificial Intelligence (IAI) and eXplainable (XAI) models for data imputations numerical or categorical hydroclimatic predictions from nonlinearly combined multidimensional predictors. The AI considered in this paper involve Extreme Gradient Boosting, Light Categorical Extremely Randomized Trees, Random Forest. These can transform into XAI when they are coupled with explanatory methods such as Shapley additive explanations local interpretable model-agnostic explanations. highlights that IAI capable unveiling rationale behind while discovering new knowledge justifying AI-based results, which critical enhanced accountability AI-driven predictions. also elaborates importance domain interventional modeling, potential advantages disadvantages hybrid non-IAI predictive unequivocal balanced decisions, choice performance versus physics-based modeling. concludes a proposed framework to enhance interpretability explainability applications.

Language: Английский

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Assessment of groundwater potential modeling using support vector machine optimization based on Bayesian multi-objective hyperparameter algorithm

Applied Soft Computing, Journal Year: 2022, Volume and Issue: 132, P. 109848 - 109848

Published: Nov. 25, 2022

Language: Английский

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Groundwater Potential Mapping in Hubei Region of China Using Machine Learning, Ensemble Learning, Deep Learning and AutoML Methods

Natural Resources Research, Journal Year: 2022, Volume and Issue: 31(5), P. 2549 - 2569

Published: July 9, 2022

Language: Английский

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Spatial prediction of groundwater potential and driving factor analysis based on deep learning and geographical detector in an arid endorheic basin

Ecological Indicators, Journal Year: 2022, Volume and Issue: 142, P. 109256 - 109256

Published: Aug. 9, 2022

Language: Английский

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Machine learning and GIS-RS-based algorithms for mapping the groundwater potentiality in the Bundelkhand region, India

Ecological Informatics, Journal Year: 2023, Volume and Issue: 74, P. 101980 - 101980

Published: Jan. 5, 2023

Language: Английский

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