Explainable ML for Irrigation Water Quality Prediction in the Sedrata Aquifer, Plain (Algeria)

Accurate assessment of irrigation water quality is essential for sustainable groundwater management in semi-arid regions. Conventional Irrigation Water Quality Index (IWQI) assessment requires multiple physicochemical measurements and manual computation of a composite index, which can be time-consuming, costly, and difficult to scale across repeated monitoring campaigns. Machine learning (ML) approaches may provide a practical surrogate for reconstructing IWQI from a reduced set of routinely measured variables. This study developed an interpretable ML framework for IWQI reconstruction in the shallow phreatic aquifer of the Sedrata Plain, northeastern Algeria. One hundred groundwater samples were collected from 25 open wells across four seasonal campaigns (February 2023–May 2024). IWQI was calculated from EC, Na+, Cl−, HCO3−, and SAR using the weighted aggregation approach of Meireles et al. Six ML algorithms were assessed using nested Recursive Feature Elimination with Cross-Validation (RFECV) within a Leave-One-Campaign-Out (LOCO) validation framework, with predictive performance evaluated using R2, RMSE, and MAE. IWQI values ranged from 28.72 to 76.28, with 8%, 56%, 28%, and 8% classified as low, moderate, high, and severe restriction, respectively. Extreme Gradient Boosting (XGBoost) achieved the highest LOCO performance (R2 = 0.732 ± 0.109; RMSE = 5.075 ± 1.403 IWQI units), compared with Multiple Linear Regression (MLR) (R2 = 0.563 ± 0.304). SHapley Additive exPlanations (SHAP) identified Cl−, EC, and SAR as the dominant predictors. The findings demonstrate the potential of an interpretable ML framework for grouped-validation IWQI reconstruction from routinely measured variables, supporting efficient irrigation-water quality screening. External validation is required before wider application.