Enhancing spatial prediction of groundwater-prone areas through optimization of a boosting algorithm with bio-inspired metaheuristic algorithms

Applied Water Science, Год журнала: 2024, Номер 14(11)

Опубликована: Окт. 30, 2024

Язык: Английский

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Identifying groundwater potential zones in a typical irrigation district using the geospatial technique and analytic hierarchy process

Geocarto International, Год журнала: 2025, Номер 40(1)

Опубликована: Янв. 27, 2025

Язык: Английский

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A Hybrid Approach for Assessing Aquifer Health Using the SWAT Model, Tree-Based Classification, and Deep Learning Algorithms

Water, Год журнала: 2025, Номер 17(10), С. 1546 - 1546

Опубликована: Май 21, 2025

Aquifer health assessment is essential for sustainable groundwater management, particularly in semi-arid regions with challenging geological conditions. This study presents a novel methodology assessing aquifer the Barakar River Basin, hard-rock terrain, by integrating tree-based classification, deep learning, and Soil Water Assessment Tool (SWAT) model. Employing Random Forest, Decision Tree, Convolutional Neural Network (CNN) models, research examines 20 influential factors, including hydrological, water quality, socioeconomic variables, to classify into four categories: Good, Moderately Semi-Critical, Critical. The CNN model exhibited highest predictive accuracy, identifying 33% of basin as having good health, while Forest assessed 27% Critical heath. Pearson correlation analysis CNN-predicted indicates that recharge (r = 0.52), return flow 0.50), fluctuation 0.48) are most positive factors. Validation results showed performed strongly, precision 0.957, Area Under Curve–Receiver Operating Characteristic (AUC-ROC) 0.95, F1 score 0.828, underscoring its reliability robustness. Geophysical Electrical Resistivity Tomography (ERT) field surveys validated these classifications, high- low-aquifer zones. enhances understanding dynamics robust broader applicability management worldwide.

Язык: Английский

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Assessing the influence of climate and land use/land cover changes on groundwater levels in arid and semi-arid regions: a geoinformatics-based machine learning approach in the Iraqi Kurdistan Region

GeoJournal, Год журнала: 2025, Номер 90(3)

Опубликована: Май 26, 2025

Язык: Английский

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