Optimized Groundwater Vulnerability Assessment Using Machine Learning: A Case Study of Luyi County, China

Groundwater vulnerability assessment is crucial for sustainable water resources management and pollution prevention. Taking Luyi County, Henan Province, China, as the study area, this study applies three supervised machine learning algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—to establish classification models using nitrate nitrogen (NO3–N) concentrations above 10 mg/L as the target variable. The predicted probability of contamination is adopted as an indicator of groundwater vulnerability. Model performance was comprehensively assessed using multiple evaluation metrics. The results show that all three models exhibited stable and strong predictive performance, with Area Under the Curve (AUC) values ranging from 0.91 to 0.94 and accuracy exceeding 86.5%. Pearson and Spearman correlation analyses were performed between observed NO3–N concentrations from 77 monitoring wells and the groundwater vulnerability results, indicating overall better performance than the traditional index-overlay method. Feature importance analysis based on the RF and XGBoost models suggests that aquifer hydraulic conductivity is the most critical controlling factor, followed by aquifer thickness and recharge, whereas land use and the remaining indicators exhibit comparatively lower contributions. The resulting vulnerability maps indicate that areas with high groundwater vulnerability are mainly concentrated in the western and southeastern parts of the study area, where agricultural activities are relatively intensive.