Aquifer Stress Assessment in Hardrock Regions of the Chotanagpur Plateau Using Integrated SWAT and Deep Learning Models

Groundwater resources in hard-rock terrains are particularly susceptible to stress due to their intricate geological formations and limited recharge capacity. This study presents a novel methodology for assessing aquifer stress within the Barakar River Basin of the Chotanagpur Plateau, leveraging the integration of the Soil and Water Assessment Tool (SWAT) and advanced deep learning models. A comprehensive evaluation was conducted using 20 hydrogeological and socio-economic parameters, including precipitation, slope, land use, and aquifer lithology. Deep learning techniques, notably Convolutional Neural Networks (CNN), were utilised to classify aquifer stress zones into four categories: Low Stress, Moderate Stress, Semi-Critical, and Critical. The CNN model demonstrated superior performance, achieving an accuracy of 94% and effectively capturing aquifer conditions' spatial and temporal dynamics. Field validation via Electrical Resistivity Tomography (ERT) surveys substantiated the reliability of the model's predictions. Findings indicate that approximately 34% of the basin experiences moderate to critical stress levels, underscoring the urgency for targeted management strategies. This integrated approach offers a scalable and robust framework for sustainable groundwater management in hard-rock terrains, with significant implications for mitigating global water scarcity.