Advancing Deltaic Aquifer Vulnerability Mapping to Seawater Intrusion and Human Impacts in Eastern Nile Delta: Insights from Machine Learning and Hydrochemical Perspective

Groundwater salinization is a critical issue in deltaic aquifers, particularly in semi-arid regions, due to seawater intrusion (SWI) and human activities. Effective Groundwater Vulnerability (GwV) mapping remains challenging in predicting salinization risks globally. This study integrates the GALDIT-NUTS framework with machine learning models—Random Forest Regression (RFR) and Generalized Linear Models (GLMs)—as well as hydrogeochemical analysis to address this challenge. Focusing on the Sharqia aquifer in Egypt’s eastern Nile Delta, the study refines GwV indices derived from GALDIT-NUTS factors using a conditioned vulnerability index (CVI) based on electrical conductivity (EC) measurements. The RFR model (R² = 0.995, RMSE = 0.014) outperformed GLM (R² = 0.942, RMSE = 0.052) and the basic GALDIT-NUTS model, confirmed by Pearson correlation analysis (RF: r = 0.995, GLM: r = 0.924, basic: r = 0.603). The refined GALDIT-NUTS-RFR map accurately pinpointed high GwV areas affected by SWI, characterized by high TDS, Na+, Cl⁻, and Sr levels, and a low Na/Cl ratio, indicating Na-Cl type water. Moderately vulnerable zones, mostly in central areas, showed higher Na/Cl ratios, possibly linked to sewage or fertilizer inputs. The least vulnerable zones, located in southern regions, exhibited freshwater facies, with Ca-HCO₃ water type and low Seawater Mixing Index (SMI). These findings are crucial for developing effective groundwater management strategies in stressed deltaic regions worldwide.