Machine Learning Prediction of Groundwater Contamination Vulnerability Using Hydrogeophysical Indicators in Ibadan, Nigeria

Abstract

Groundwater contamination threatens aquifer sustainability in crystalline basement land-scapes experiencing rapid urban growth. This study employs machine learning to estimategroundwater contamination vulnerability in Ibadan, southwestern Nigeria, using hydrogeo-physical indicators from 353 Vertical Electrical Sounding (VES) surveys. Key parameters—overburden thickness, aquifer resistivity, basement resistivity, longitudinal conductance,and transverse resistance—characterized aquifer protective capacity. Conventional indices (Groundwater occurrence, Overlying lithology, Depth to groundwater [GOD] and modified GODT including overburden thickness) defined vulnerability classes as supervised labels.The Random Forest classifier achieved accuracy = 0.94, precision = 0.94, recall = 0.93,F1-score = 0.93, AUC = 0.95. Overburden thickness and longitudinal conductance were the most significant predictors. The model identified fifteen high-vulnerability zones versus nine from conventional GODT, demonstrating ability to capture nonlinear interactions conventional methods miss. This approach contributes to evidence-based water resource management in urban aquifer systems.