Positive–Unlabeled Learning-Based Hybrid Models and Interpretability for Groundwater Potential Mapping in Karst Areas

Despite the increasing adoption of machine learning and data-driven models for predicting regional groundwater potential (GWP), exploration geoscientists have recognized that these models still face various challenges in their predictive precision. For instance, the stochastic uncertainty associated with incomplete groundwater investigation inventories and the inherent non-transparency characteristic of machine learning models, which lack transparency regarding how input features influence outcomes, pose significant challenges. This research constructs a bagging-based learning framework that integrates Positive–Unlabeled samples (BPUL), along with ex-post interpretability, to map the GWP of the Lijiang River Basin in China, a renowned karst region. For this purpose, we first aggregated various topographic, hydrological, geological, meteorological, and land conditional factors. The training samples were enhanced with data from the subterranean stream investigated in the study area, in addition to conventional groundwater inventories such as wells, boreholes, and karst springs. We employed the BPUL algorithm with four different base learners—Logistic Regression (LR), k-nearest neighbor (KNN), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM)—and model validation was conducted to map the GWP in karst regions. The findings indicate that all models exhibit satisfactory performance in GWP mapping, with the hybrid ensemble models (RF-BPUL and LightGBM-BPUL) achieving higher validation scores. The model interpretation of the aggregated SHAP values revealed the contribution patterns of various conditional factors to groundwater distribution in karst zones, emphasizing that lithology, the multiresolution index of valley bottom flatness (MRVBF), and the geochemical element calcium oxide (CaO) have the most significant impact on groundwater enrichment in karst zones. These findings offer new approaches and methodologies for the in-depth exploration and scientific prediction of groundwater potential.