Groundwater potential maps are important tools for the sustainable management of water resources, especially in agricultural producing countries like Vietnam. Here, we describe the development and application of a spatially explicit ensemble modeling framework that allows for analyzing spatially explicit data for estimating groundwater potential across the Kon Tum Province, Vietnam. Based on this framework, the Naïve Bayes (NB) method was integrated with the Bagging (B), AdaBoost (AB), and Rotation Forest (RF) ensemble learning techniques to develop three ensemble models, namely BNB, ABNB, and RFNB. A suite of well yield data and thirteen explanatory variables (i.e., elevation, aspect, slope, curvature, river density, topographic wetness index, sediment transport index, soil type, geology, land use, rainfall, and flow direction and accumulation) were incorporated into the modeling processes over the independent training and validation levels of the single NB model and its three ensembles. Several performance metrics (i.e., area under the receiver operating characteristic curve (AUC), root mean square error (RMSE), accuracy, sensitivity, specificity, negative predictive value, and positive predictive value) demonstrated that the three ensemble models successfully surpassed the single NB model in groundwater potential mapping. The ensemble RFNB model with AUC = 0.849, accuracy = 83.33%, sensitivity = 100%, specificity = 75%, and RMSE = 0.406 exhibited the most accurate performance for mapping groundwater potential in the Kon Tum Province, followed by the ABNB (AUC = 0.844), BNB (AUC = 0.815), and single NB (AUC = 0.786) models, respectively. Further, the correlation based feature selection method identified elevation, slope, land use, rainfall, and STI as the most useful explanatory variables for explaining the distribution of groundwater potential in the Kon Tum Province. The methodology proposed in this case study and the produced potential maps enable managers to align water use patterns with the shared benefits and costs of different users and to develop strategies for sustainable groundwater exploitation, preservation, and management.

地下水潜力图是水资源可持续管理的重要工具，尤其是在越南等农业生产国。在这里，我们描述了空间显式集合建模框架的开发和应用，该框架允许分析空间显式数据以估算越南 Kon Tum 省的地下水潜力。基于此框架，朴素贝叶斯（NB）方法与 Bagging（B）、AdaBoost（AB）和旋转森林（RF）集成学习技术相结合，开发出三种集成模型，即 BNB、ABNB 和 RFNB。一套井产量数据和 13 个解释变量（即高程、坡向、坡度、曲率、河流密度、地形湿度指数、泥沙输送指数、土壤类型、地质、土地利用、降雨量、和流向和累积）被纳入建模过程中，独立训练和验证级别的单个 NB 模型及其三个集合。多项性能指标（即接收者操作特征曲线下面积 (AUC)、均方根误差 (RMSE)、准确度、灵敏度、特异性、阴性预测值和阳性预测值）表明，三个集成模型成功超越了单一模型。地下水潜力测绘中的 NB 模型。AUC = 0.849、准确度 = 83.33%、灵敏度 = 100%、特异性 = 75% 和 RMSE = 0.406 的集合 RFNB 模型在绘制 Kon Tum 省地下水潜力图时表现出最准确的性能，其次是 ABNB（AUC = 0.844)、BNB (AUC = 0.815) 和单个 NB (AUC = 0.786) 模型。更远，基于相关性的特征选择方法将高程、坡度、土地利用、降雨量和 STI 确定为解释 Kon Tum 省地下水潜力分布的最有用的解释变量。本案例研究中提出的方法和生成的潜在地图使管理人员能够将用水模式与不同用户的共享收益和成本保持一致，并制定可持续地下水开发、保护和管理的策略。