## 1 Introduction  
Groundwater demand has increased because groundwater is one of the most important natural resources, supporting human health, economic development, and ecological diversity. Moreover, it is readily obtainable anywhere, provides excellent water quality, and has low development costs [1]. Surface water accounts for only 0.3% of the freshwater that exists on Earth and groundwater can be recharged each year by rainfall. However, the rapid increase in human population has increased the use and demand for groundwater resources for drinking, agricultural, and industrial purposes. Therefore, to ensure that a secure amount of water is available, systematic development and management planning should be established. In Korea, the occupancy rate of groundwater is 11% [2], and public water supplies (e.g., water supplies of towns) of groundwater are only 5% of total water use. Considering that the amount of groundwater used in Korea increased by more than 210% [3] between 1994 and 2008, development and utilization at the national level currently do not meet people's growing needs. Hence, the development of reliable analytical methods and models for predicting locations that have GPP is urgently needed for systematic development, efficient management, and sustainable use of groundwater resources. GIS and other technologies have great potential for use in studies of groundwater hydrology, and thus many researches have used GIS and remote sensing techniques along with thematic layers such as geomorphology, drainage pattern, lineament, lithology, and soil for that purpose. Some studies have applied probabilistic models such as multi-criteria decision analysis and weights-of-evidence modeling for groundwater-potential mapping [4]. The other approaches have conducted numerical modeling, decision trees, fuzzy logic, and analytic hierarchical process analyses [5]. Some researchers have also integrated GIS, remote sensing, and geophysical surveys to derive additional thematic layers of surface parameters such as resistivity, aquifer thickness, or fault maps [6]. However, such studies had limitations because they used indirect indicators such as yield, groundwater depth, resistivity, and spring location, rather than hydraulic constants such as SPC and transmissivity (T). Furthermore, past studies have not validated the results by comparison with other datasets. The decision tree is a decision support method that uses a tree-like model of decisions. It is a hierarchical model composed of decision rules that recursively splits independent variables into homogeneous zones [7]. They split to favorable and non-favorable for groundwater productivity based on the relationship between SPC and each hydrogeological factor. The SPC is related to groundwater productivity and the SPC was measured and calculated. Moreover, an exhaustive search tends to select variables that afford more splits. Also, unlike other statistical models, a decision tree makes no statistical assumptions, can handle data represented on different measurement scales, and is computationally fast [8]. Therefore, in this study, a data-driven application of the decision tree methodology was provided to obtain more accurate and reliable estimates. More specifically, the main novelty of the study was to apply the decision tree model to make a better GPP map using the SPC value for the criterion variable in the Boryeong and Pohang cites, Korea. The study flow, shown in Figure 1 is as follows: (1) Assembly of a spatial database. A total of 124 SPC data points (Boryeong) and 83 SPC data (Pohang) points were used to create a spatial database using GIS. Topography, hydrogeology, lineament, and soil data were collected and input into a spatial database. (2) The SPC data of \(\geq\) 4.55 m\({}^{3}\)/day/m (Boryeong) and \(\geq\) 6.25 m\({}^{3}\)/day/m (Pohang) were randomly divided into training data (50% of well locations) and validation data (50% of well locations). (3) Topography, lineament, hydrogeology, and soil datasets were compiled in a spatial database.