Enhancing Groundwater Detection Through Resistivity method: A Machine Learning Approach

Abstract—Access to clean water and affordable electricity is vital for sustainable living. This study introduces a Machine Learning (ML) framework to locate groundwater concurrently. The ML framework integrates diverse data modalities into a unified hypersurface, accommodating numeric and categorical field observations. These modalities include field measurements and data-driven machine learning outputs. Applied to various locations, the ML workflow predicts geophysical, geologic, and hydrogeologic features. Despite challenges like data disparity and spatial limitations, our model accurately identifies hidden groundwater. The study contributes insights/into sustainable resource/management/and demonstrates the applicability of/the ML framework to address local challenges. This research presents a promising approach to address water and energy resource needs in India, aligning with the country's sustainability goals. Keywords—"Groundwater localization, machine learning framework, data-driven predictions, water resource management, spatial data analysis."