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.

Mining plays a critical role in economic development, yet it presents significant safety risks and environmental challenges. Balancing worker safety and environmental preservation while maintaining sustainable practices is a pressing concern for the modern mining industry. This study explores the use of integrated geophysical techniques to enhance mine safety assessments and support sustainable mining operations. By combining methods such as Electrical Resistivity Tomography (ERT), seismic surveys, Ground-Penetrating Radar (GPR), magnetic surveys, and gravity surveys, the approach provides a comprehensive and non-invasive understanding of subsurface conditions in active mining areas. The integrated geophysical framework enables real-time monitoring of underground stability, facilitating the detection of hazards such as ground subsidence, roof falls, sinkholes, potholes, and unstable fault zones. These techniques deliver high-resolution data, revealing surface and deep structural instabilities, which allow for proactive risk management and inform strategic decision-making processes. The research demonstrates how continuous geophysical monitoring reduces accident risks, enhances operational efficiency, and ensures long-term sustainability in mining practices. This study further highlights the broader benefits of adopting integrated geophysical methods, emphasizing their role in minimizing environmental impact while improving workplace safety. By integrating advanced geophysical techniques into mining operations, this research establishes a model for ensuring both worker safety and environmental preservation, aligning with global sustainable development goals. The proposed approach bridges the gap between mining safety and sustainability, showcasing the potential of technological innovation to mitigate the inherent risks of mining activities.