## 1 Introduction  
As pointed out by many researchers, one of the most important natural resource worldwide is groundwater, with one third of the world's population depending on it [1; 2; 3; 4]. Several areas in the world are subject to overexploitation of groundwater, undergoing water shortages as a result of a difference between water supply and demand [5]. It is also well established that the demand for groundwaterwill increase substantially in the following years, mainly due to the growing population and economic development [6; 7; 8; 9; 10]. According to [PERSON] and [PERSON] [11], even though the supply of water is mainly controlled by climatic parameters, the management and the followed practices significantly influence the availability of water. In the case of groundwater resources, inappropriate management may result in the deterioration of water resources but also a decrease in the quality of groundwater [12]. Similar with the rest of the world, China faces increasing consumption of groundwater, making imperative the application of accurate methods for assessing groundwater potential [13; 14; 15; 16]. Groundwater spring potential mapping has been recognized as an investigation practice, the outcomes of which provide useful data inputs concerning groundwater management projects [17]. Identifying areas with high probability concerning the presence of groundwater springs assists in developing appropriate groundwater exploitation and groundwater resources conservation programs [18; 19]. Over the past two decades, geographical information systems (GIS) and remote sensing techniques (RS) have been the main investigation tools concerning groundwater spring potential mapping [4; 17; 18; 20; 21; 22]. Successful examples of studies concerning groundwater potential mapping involve bivariate and multivariate methods and specifically applications of frequency ratio (FR) [13; 17; 23], analytical hierarchy process (AHP) [24; 25; 26; 27; 20], weight of evidence (WofE) [18; 23; 28; 29], evidential belief function (EBF) [19; 30; 31; 32; 33], and logistic regression (LR) [28; 34; 35]. Similar, machine learning methods have been introduced as an alternative option for groundwater potential, mapping mainly involving tree-based methods, such as classification and regression tree (CART) [36] and random forest (RF) [19; 26; 37], and neural network-based methods, such as artificial neural network (ANN) [18; 38; 39] and support vector machine (SVM) [40]. Other notable examples of machine learning methods that have been utilized in groundwater potential mapping assessments are the implementations of naive Bayes (NB) [41] and K-nearest neighbor (KNN) [42]. Quite recently, new hybrid and ensemble methods have been applied in groundwater mapping studies, showing in most cases enhanced performance than single predictive models [12]. [PERSON] et al. [43] produced groundwater potential maps integrating WofE with LR and functional tree (FT) models, the validation of which clearly highlighted the efficacy of the integrated models. The authors reported that the integrated models provided better results, overcoming the drawbacks of bivariate statistics and machine learning. [PERSON] et al. [44] proposed five hybrid artificial intelligence methods, integrating an adaptive neuro-fuzzy inference system (ANFIS) and meta-heuristic algorithms. The outcomes of their study illustrated that by applying the novel hybrid models they could produce more accurate groundwater potential models. [PERSON] et al. [33] proposed an ensemble method, integrating EBF and boosted regression tree (BRT), reporting that the EBF-BRT model was capable of providing highly accurate results. The authors suggest that the produced model improves the weak points of each method, while taking advantage of the ability of the methods to analyze the relation of groundwater with each groundwater-related variable and with each class of the groundwater-related variable. In a similar study, [PERSON] et al.