Due to the rapidly increasing demand for groundwater, as one of the principal freshwater resources, there is an urge to advance novel prediction systems to more accurately estimate the groundwater potential for an informed groundwater resource management. Ensemble machine learning methods are generally reported to produce more accurate results. However, proposing the novel ensemble models along with running comparative studies for performance evaluation of these models would be equally essential to precisely identify the suitable methods. Thus, the current study is designed to provide knowledge on the performance of the four ensemble models i.e., Boosted generalized additive model (GamBoost), adaptive Boosting classification trees (AdaBoost), Bagged classification and regression trees (Bagged CART), and random forest (RF). To build the models, 339 groundwater resources’ locations and the spatial groundwater potential conditioning factors were used. Thereafter, the recursive feature elimination (RFE) method was applied to identify the key features. The RFE specified that the best number of features for groundwater potential modeling was 12 variables among 15 (with a mean Accuracy of about 0.84). The modeling results indicated that the Bagging models (i.e., RF and Bagged CART) had a higher performance than the Boosting models (i.e., AdaBoost and GamBoost). Overall, the RF model outperformed the other models (with accuracy = 0.86, Kappa = 0.67, Precision = 0.85, and Recall = 0.91). Also, the topographic position index’s predictive variables, valley depth, drainage density, elevation, and distance from stream had the highest contribution in the modeling process. Groundwater potential maps predicted in this study can help water resources managers and policymakers in the fields of watershed and aquifer management to preserve an optimal exploit from this important freshwater.

由于对作为主要淡水资源之一的地下水的需求迅速增长，迫切需要开发新颖的预测系统，以更准确地估算地下水的潜力，从而进行明智的地下水资源管理。据报道，集成机器学习方法可产生更准确的结果。但是，提出新的集成模型以及进行比较研究以评估这些模型的性能对于准确地确定合适的方法同样至关重要。因此，本研究旨在提供关于四种集成模型的性能的知识，即Boosted广义加性模型（GamBoost），自适应Boosting分类树（AdaBoost），Bagged分类和回归树（Bagged CART）和随机森林（ RF）。要建立模型，使用了339个地下水资源的位置和空间地下水潜力条件因子。此后，应用递归特征消除（RFE）方法来识别关键特征。RFE指出，用于地下水潜力建模的最佳要素数量是15个变量中的12个变量（平均精度约为0.84）。建模结果表明，Bagging模型（即RF和Bagged CART）具有比Boosting模型（即AdaBoost和GamBoost）更高的性能。总体而言，RF模型优于其他模型（准确度= 0.86，Kappa = 0.67，精确度= 0.85和召回率= 0.91）。此外，地形位置指数的预测变量，谷底深度，排水密度，高程和与河流的距离在建模过程中具有最大的贡献。