Enhancing aquifer protective capacity prediction over Ibeator and environ, Southeastern Nigeria using artificial neural networks and multivariate linear regression analysis

Abstract Aquifer protection is essential for securing a sustainable supply of clean water. This study integrates an artificial neural network (ANN) model, identifying non-linear connections, with multivariate linear regression (MLR) analysis to improve predictions of aquifer protective capacity and assess vulnerability. Twelve vertical electrical soundings (VES) were conducted with a maximum electrode spacing of 250 m. Aquifer parameters derived from the VES dataset were analyzed using ANN to capture complex patterns. The ANN model, trained on historical data, learned the relationship between input variables and protective capacity. MLR analysis identified influential factors affecting vulnerability. Results reveal varying aquifer depths, with Umudime being the deepest and western parts having the shallowest depths. The resistivity map shows high values around Okorobi and Uhuala and low values in eastern to northeastern parts. Hydraulic conductivity and 3D subsurface models exhibit an inverse relationship with resistivity. Transmissivity and storativity maps exhibit similar patterns. MLR outperforms ANN in predicting resistivity, transmissivity, and storability, indicating high forecasting accuracy for aquifer protective capacity. Input parameters' contribution levels follow a specific order for different aquifer properties. R 2 Value 0.0869, indicating a weak correlation between the predicted and actual values in ANN model while R 2 Value 0.9775 in MLR model shows a strong correlation and much better performance than the ANN model. The results of the modeling suggest that both the ANN and MLR models have shown promising effectiveness and accuracy in predicting aquifer parameters, aiding decision-makers in implementing targeted protection measures, predicting aquifer parameters, providing insights for effective management strategies.