Groundwater potential mapping in the Central Highlands of Vietnam using spatially explicit machine learning

The sustainability of water resource management remains challenging in many regions around the world. Yet while the significance of groundwater potential maps in water resource management is well known, no agreed-upon approach has been suggested for the production of reliable, accurate maps of groundwater potential. In this study, we evaluated the Partial Decision Tree (PART), Fuzzy Unordered Rule Induction Algorithm (FURIA), Multilayer Perception Network (MLP), Forest by Penalizing Attributes (FPA), and an ensemble version of the FPA method with the Decorate ensemble learning techniques (DFPA) for their capability to explore the associations between the locations of groundwater wells and a set of geo-environmental variables for the prediction of the potential for groundwater occurrence. We applied the methods to a spatially explicit dataset from five provinces of the Central Highlands, Vietnam. The results revealed that rainfall, land use/cover, elevation, and river density contributed most to groundwater potential in the study area. The ensemble model, i.e., DFPA, achieved greater goodness-of-fit and predictive ability than the single models. The ensemble DFPA model with accuracy = 70%, ROC-AUC = 0.77, RMSE = 0.44 provided the most accurate prediction of groundwater potential in the study area, followed by the FPA (ROC-AUC = 0.76), PART (ROC-AUC = 0.72), FURIA (ROC-AUC = 0.7), and MLP (ROC-AUC = 0.69) models, respectively. The ensemble DFPA model classified 34.7, 44.1, and 21.2% of the Central Highlands into low, moderate, and high potential categories, respectively. We experimentally showed that ensemble modeling is promising as a supporting tool in helping decision-makers, stakeholders, and researchers promote strategies for sustainable water resources management.