## 1 Introduction  
Groundwater is one of the most valuable resources of water supply for agricultural, urban, and industrial activities in many parts of the world [1]. Groundwater contributes to the economic development and biodiversity of an area [2]. The effective management of groundwater is critical for balancing different demands on water. Groundwater management is becoming progressively challenging due to rapid population growth and occurrences of intermittent and prolonged drought periods [3; 4; 5; 6] that have mounted increasing pressure on groundwater resources worldwide [7]. Therefore, further research on the exploration and estimation of groundwater potential and resources is necessary for proper water management of an area. Among different management strategies, potential groundwater mapping is an effective approach that can assist managers to adopt more efficient management plans [8; 9; 10; 11; 12]. Various methods have been suggested and used for producing groundwater potential maps. They range from the traditional surface and sub-surface potential assessment methods to the recent predictive models derived by machine learning methods. The surface and sub-surface methods include esoteric, geomorphologic, geological, soil and micro-biological, surface geophysical test drilling of boreholes, and geophysical logging techniques [13]. Although these methods are highly efficient for obtaining an accurate estimate of groundwater potential, the lack of advanced technologies and sufficient hydrogeological data preclude their use in many countries. Machine learning methods and GIS-based data processing techniques have provided opportunities for developing powerful tools for the assessment of groundwater potential in a time- and cost-effective manner. Examples of these methods include a classification and regression tree (CART) [14], logistic model tree (LMT) [11], multivariate adaptive regression spline (MARS) [15], random forest (RF) [16], boosted regression tree (BRT) [17], maximum entropy (ME) [18], support vector machine (SVM) [19; 20], artificial neural network (ANN) [20], multivariate adaptive regression [21], and maximum entropy [22]. Recent advancement in groundwater potential mapping is the combined use of different machine learning methods towards developing hybrid ensemble machine learning models for obtaining the most accurate results. [PERSON] et al. [23] proposed the integrated application of J48 decision trees with the random subspace (RSS), rotation forest (RF), AdaBoost, bagging, and dagging to identify the groundwater potential zones in Wuqi County, China. [PERSON] et al. [14] improved performances of the BRT, CART, and RF classifiers using a rotation forest ensemble technique for modeling groundwater potential in Meshgin Shahr, Iran. [PERSON] et al. [24] developed an ensemble model based on the RF classifier and the RSS ensemble technique to map the groundwater potential in Kurdistan, Iran. [PERSON] et al. [25] integrated the best first decision tree with the AdaBoost, multiboosting, and bagging ensembles for groundwater potential mapping in the Yasui-Dena area, Iran. [PERSON] et al. [26] developed a hybrid model based on the SVM and genetic algorithm (GA) for groundwater potential mapping in Jerash and Ajloun, Jordan. In another study, [PERSON] et al. [19] used GA for optimizing the structure of the SVM and RF models for groundwater potential mapping in Iran. [PERSON] et al. [27] used several metaheuristic algorithms for optimizing a neuro-fuzzy model for groundwater potential mapping in Lorestan, Iran. [PERSON] et al. [28] used the whale optimization algorithm for optimizing the ANN for groundwater potential mapping. All these studies demonstrated the enhanced predictive performance of the hybrid ensemble models compared to the single models. In fact, the premise of the application of a hybrid ensemble is that groundwater potential mapping requires big data of various geo-environmental variables [11; 12; 23; 24] that largely make the single modeling approaches inefficient in many regions [29].