Explainable AI Models for Interpretable Forecasting of Groundwater Recharge Rates and Contaminant Migration in Arid Environmental Systems

Arid environmental systems, characterized by low precipitation and high evaporation, rely heavily on groundwater as the primary freshwater resource, yet face escalating threats from unpredictable recharge rates and contaminant migration driven by agricultural runoff and industrial activities. Traditional hydrological models often struggle with the nonlinear complexities of these dynamics, while black-box machine learning approaches, though powerful, lack the transparency needed for stakeholder trust and regulatory compliance. This study presents a novel suite of explainable AI (XAI) models that integrate ensemble techniques like XGBoost and LSTM networks with post-hoc interpretability tools such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to provide both high-fidelity forecasts and clear insights into underlying mechanisms.Developed for a representative arid basin akin to those in the Arabian Peninsula, the models leverage diverse datasets including satellite-derived rainfall, soil permeability profiles, isotopic tracers, and long-term well monitoring data for nitrates, heavy metals, and salinity. Recharge rates are forecasted with superior accuracy (R² > 0.93, RMSE < 2.1 mm/month), capturing episodic infiltration events, while contaminant migration is simulated through coupled advection-dispersion modules, predicting plume extents up to 7 km under high-recharge scenarios. Interpretability analyses pinpoint precipitation intensity, antecedent soil moisture, and hydraulic gradients as pivotal drivers, with force plots elucidating event-specific influences such as how flash floods dilute pollutants temporarily.These frameworks not only outperform conventional physics-based simulators like MODFLOW by 25% in predictive skill but also empower hydrologists and policymakers with actionable visualizations, uncertainty quantifications, and feature attribution maps. By demystifying AI predictions, this work advances sustainable aquifer management, supports managed recharge initiatives, and sets a benchmark for interpretable forecasting in climate-vulnerable drylands, fostering broader adoption of AI in environmental hydrology.