## 1 Introduction  
According to The United Nations World Water Development Report (WWDR) 2018, more than 2 billion people in the World do not have access to safe drinking water and sanitation. If the current levels of water pollution and consumption are not reduced, nearly one-third of the world's population will suffer under severe water stress by approximately 2050 [1]. Climate change, increasing water scarcity, environmental degradation, population growth, and urbanization are already posing challenges for surface water supply systems [2]. Other means of meeting the demand for freshwater, such as using groundwater, will have to be determined. Presently around 20% of the total groundwater resources are being used globally [3]. Groundwater is a very efficient resource and can be used for agriculture, forestry, rearing of livestock, industrial purposes, and as a drinking water source for the community [4]. One of the most valuable benefits of groundwater is that it is less susceptible to environmental pollution than surface water [5]. Therefore, effort to find high quality groundwater is growing globally [6]. In South Korea, the rate of groundwater use has increased, and yet its supply does not meet the needs of the people [7]. Therefore, in South Korea, studies to evaluate the sustainability of groundwater in order to improve the use of groundwater and to evaluate the potential of groundwater in order to efficiently manage groundwater should be encouraged. With scientific advancement in terms of GIS technology, various spatial modeling techniques have been developed and applied to evaluate the potential of groundwater productivity in recent years. GIS and remote sensing can be used to detail large areas in a more cost-effective manner [8; 9; 10; 11; 12; 13; 14; 15; 16]. In contemporary studies, Frequency Ratio (FR) [17; 18; 19], Random Forest (RF) [20; 21; 22; 23], Logistic Regression (LR) [24; 25; 26], Boosted Regression Tree (BRT) [27; 28], Support Vector Machine (SVM) [13; 29; 30; 31], Artificial Neural Network (ANN) [32; 33; 34; 35; 36], Weights of Evidence (WoE) [37; 38; 39], Evidential Belief Function (EBF) [40; 41; 42], and various other ensemble models have been applied for Groundwater Productivity Potential (GPP) mapping. Ensemble models such as RF and BRT were also used to study ecology, landslide, subsidence, flood vulnerability, and etc. [43]. [PERSON], et al. [44] evaluated the groundwater potential of Ghana's Nabogo basin using the weighted overlay technique. They achieved more accurate and reliable results by utilizing the commonly used specific capacity (SPC) values as wells as transmissivity (T) values. [PERSON], [PERSON], [PERSON] and [PERSON] [24] performed GPP mapping using the LR and Multivariate Adaptive Regression Splines (MARS) models; these showed 84% and 87% verification accuracies, respectively. [PERSON], et al. [45] analysed the relationships between the groundwater pumping capacity and related factors using the FR and Boosted Classification Tree (BCT) models in Goyang-si in Gyeonggi-do province, South Korea. The results of the accuracy rates were 68.31% and 69.39%, respectively. In the previous studies, various ensemble models were used to predict GPP, and their accuracy showed a reliable level of results (approximately, >65%). However, for high accuracy of results, many studies will need to be carried out through the application of various topographical, geological and hydrological data (e.g., data obtained through remote sensing images) and various models that have not been utilized previously. The purpose of the present study was to apply and analyze the LR (statistical model), RF & BRT (machine learning models) and determine their ability to perform accurate and effective GPP mapping. In addition, this study also intended to identify the important factors affecting GPP. Numerous preceding studies have used various models to analyze GPP, however, the LR, RF and BRT models have not yet been widely used.