Enhanced groundwater vulnerability assessment to nitrate contamination in Chongqing, Southwest China: Integrating novel explainable machine learning algorithms with DRASTIC-LU

Groundwater vulnerability to nitrate assessment serves as a measure of potential groundwater nitrate pollution in a target area. This study applies the DRASTIC-LU framework, nitrate distribution data, and three machine learning models (RF, XGB, SVM) to classify nitrate levels (exceeding 10 mg/L as nitrogen) in Chongqing, China. Model evaluation uses accuracy and F1 score metrics, with RF achieving the highest accuracy (92.9%), kappa (0.857), and AUC (0.948) on test dataset. Furthermore, the SHAP interpreter revealed that aquifer conductivity, lithology, agricultural activities, areas with high-intensity development, and groundwater recharge are the most influential indicators of groundwater vulnerability. The final groundwater vulnerability level distribution map, with a resolution of 1 km × 1 km, reveals that high and extremely high vulnerability levels are concentrated in areas with high-intensity urban development and karst trough valleys in the southeastern, northeastern, and central urban areas. This work represents the first attempt of using machine learning models for groundwater vulnerability assessment in the Chongqing region. It provides theoretical support for the construction layout of groundwater monitoring stations and the prevention and control of groundwater pollution in the future. HIGHLIGHTS Predicting groundwater nitrate vulnerability in Chongqing region using DRASTIC-LU framework and machine learning models.; Random Forest outperforms other machine learning approaches.; Aquifer conductivity, lithology, agricultural activities and areas with high-intensity development predictors were most influential explanatory factors.; Distribution of groundwater nitrate vulnerability map at 1 km resolution.; Groundwater nitrate vulnerability map reveal the high vulnerability levels distributed karst trough valleys in the southeastern, northeastern, and central urban areas.;