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