Integration of Geophysics, AI, and GIS for Groundwater Contamination Assessment in Zliten

Abstract

Environmental geophysics provides non-invasive techniques for assessing groundwater quality and contamination risks. This research integrates electrical resistivity and magnetic methods, based on findings from previous studies, to model groundwater rise and pollution in Zliten, Libya. The adapted methodology demonstrates the efficiency of resistivity and magnetic surveys in detecting saline intrusion, industrial pollution, and variations in the groundwater table. AI-assisted classification (ANN and SVM) improved interpretation accuracy, providing a practical and scalable framework for monitoring groundwater in semi-arid coastal environments.