<![CDATA[Groundwater management is essential as more than one-third of the world's population relies on groundwater as a source of freshwater. Exploration of groundwater potential serves as a practical im-plementation to ensure accessibility to freshwater. Therefore, this study developed a machine learning (ML), deep learning (DL), and stacking learning (SL) based model for groundwater potential mapping in Trenggalek Regency, Indonesia. A total of 740 spring locations were used as training data, and 18 variables were considered in the modelling. The eighteen parameters were classified into geological, topographic, land cover, climatological, hydrological, and geophysical factors. We used several algo-rithms, including gradient boosting decision trees (GBDT), random forest (RF), recurrent neural net-work (RNN), convolutional neural network (CNN), SL GBDT-RF, SL CNN-RNN, and SL GBDT-RF-CNN-RNN. This study optimized each basic learning task through hyperparameter fine-tuning using a tree-Parzen structured estimator (TPE) method. Models were evaluated using four metrics: accuracy (Acc), Cohen's kappa (CK), Matthews correlation coefficient (MCC), and receiver operating character-istic (ROC) area under the curve (AUC). Of the seven models generated, SL GBDT-RF achieved the best performance on the test data, with Acc, MCC, CK, and AUC values of 0.957, 0.915, 0.915, and 0.990, respectively. The geological unit parameter has the highest relative contribution rate in all pre-diction models. Based on the best model, the study area is dominated by the low-potential class, accounting for 31.29%. This study contributes to providing a benchmark for the development of groundwater potential prediction using ML, DL, and SL algorithms across various case studies. In addition, this study can be used by concerned stakeholders in sustainable water resource planning, drought disaster management, and the prevention of inappropriate groundwater exploration.]]>
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