Hydrogeochemical Controls and Explainable Machine Learning for Reliable Prediction of Fluoride Contamination in Groundwater

Fluoride contamination in groundwater poses a significant public-health concern in most semi-arid areas such as the Punjab alluvial aquifers of Pakistan, with local concentrations exceeding the WHO guideline. Reliable fluoride dynamics prediction and mechanistic interpretation of fluoride is key for targeted monitoring and risk mitigation. This paper built an integrated hydrogeochemical machine learning model to predict the fluoride concentration and classify exceedance risk in the Rechna Doab aquifer Tehsil Jaranwala, Punjab, Pakistan. Nested cross-validation and independent test evaluation were performed on conventional models (linear regression, random forest, XGBoost) and a deep tabular model (FT-Transformer). Model reliability was evaluated using discrimination and probability-calibration metrics, while Shapley Additive Explanations (SHAP) and permutation importance were applied to identify the main hydrogeochemical controls on fluoride prediction. Moreover, the robustness was tested by noise sensitivity experiments. Fluoride concentrations showed a positive skewed distribution with some local exceedances related to the geogenic and hydrochemical influences. Nonlinear models greatly outperformed the linear baseline; XGBoost showed robust regression performance (test R2 = 0.878; RMSE ≈ 0.190 mg/L). The FT-Transformer showed strong exceedance-classification performance, with high sensitivity (recall = 0.875) and good probability calibration (Brier ≈ 0.021). Interpretability analyses identified EC/TDS, Mg2+, and Ca2+ as important predictors, linking fluoride enrichment to chemically evolved groundwater with reduced calcium activity, sodium enrichment, and alkalinity buffering. The proposed framework provides accurate, interpretable, and risk-oriented support for groundwater fluoride monitoring in alluvial aquifer systems.