Groundwater Potential Mapping Using Optimized Decision Tree-Based Ensemble Learning Model with Local and Global Explainability

Identifying potential groundwater areas is of great importance for its sustainable management. This study improves groundwater potential mapping in Fars province, Iran, by integrating Random Forest (RF) and Categorical gradient Boosting (CatBoost) models with a Bayesian optimization algorithm. The Boruta–XGBoost algorithm for selecting the most important features and SHapley Additive exPlanation (SHAP) values increased the local and global interpretability of the models. The results showed that the optimized CatBoost model provided a more accurate and reliable groundwater potential map with an Area Under the receiver operating characteristic Curve (AUC) of 0.8778 and a Root Mean Square Error (RMSE) of 0.3779 compared to the RF with an AUC = 0.8396 and RMSE = 0.4072. The CatBoost model also identified 80% of wells with potential 1 in the very high and high potential classes, as well as 60% of wells with potential 0 in the low and very low potential classes. SHAP analysis highlighted land use/land cover and the terrain roughness index as the most impactful features, while porosity and permeability had minimal influence. Also, the contribution of individual features for each mapping unit in the study area was calculated using SHAP analysis and a map of SHAP values was prepared. The proposed approach offers a comprehensive methodology for groundwater potential mapping, encompassing input data identification, key feature selection, machine learning model optimization, and output explanation. This effective procedure can be applied in other areas and regions, providing valuable insights for decision-makers to manage groundwater resources sustainably and ensure water security.