## 4 Data Used  
A crucial aspect in groundwater spring potential mapping process is to identify spring locations. Based on extensive field surveys conducted during 2006-2017, 66 springs were detected in the study area (Figures 1 and 3a,b). An equal number of 66 nonspring locations were randomly selected from the free of spring's space by applying the Create Random Points function found in the Data Management Tools in the ArcGIS platform [69]. The spring and nonspring locations were randomly divided into two subsets, by using the Subset tool in the Geo statistical extension package of the ArcGIS platform [69]. The first subsets consisted of 46 spring and 46 nonspring locations, 70% of the total number of springs and nonspring areas and were used for training, whereas the second subset consisted of the remaining 30% (20 spring and 20 nonspring locations) and were used for validation.  
Generally, several spring explanatory factors may influence spring occurrence, however, there are no guidelines for the selection of spring explanatory factors. Therefore, in the present study and based on the experienced gained from previous studies, 14 spring explanatory factors, including slope aspect, slope angle, plan curvature, profile curvature, elevation, stream power index (SPI), sediment transport index (STI), topographic wetness index (TWI), distance to streams, distance to roads, normalized difference vegetation index (NDVI), lithology, soil, and land use, were selected and prepared for further analysis within a GIS environment [43; 70; 71]. Eight geomorphometric factors, including slope aspect, slope angle, plan curvature, profile curvature, elevation, SPI, STI, and TWI, were extracted from the ASTER GDEM version 2 sensor ([[http://www.jspacesystems.or.jp/ersdac/GDEM/E/index.html](http://www.jspacesystems.or.jp/ersdac/GDEM/E/index.html)]([http://www.jspacesystems.or.jp/ersdac/GDEM/E/index.html](http://www.jspacesystems.or.jp/ersdac/GDEM/E/index.html))) with a resolution of 30 m. These spring explanatory factors were reclassified into categories (Table 2) based on the outcomes of frequency analysis concerning spring occurrence and also characteristics of the study area. The distance-to-streams and distance-to-roads maps were produced using the topographic maps at 1:10,000-scale. The NVDI was calculated using Landsat 8 OLI (path/row 126-33) obtained on 4 November 2017 (available at [[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn))). A lithological map was extracted from the geological map at a scale of 1:10,000 and constructed with nine classes based on lithological similarities [43; 72]. The soil types were extracted from soil maps at 1:1,000,000-scale in the study area and were classified into four classes [43; 73]. In addition, the land use map was extracted from land use maps at 1:100,000-scale with six land use types based on the supervised classification method and maximum likelihood algorithm [19]. All the spring explanatory factors were finally converted into the same spatial resolution of \(30\times 30\) m\({}^{2}\) (Figure 4).  
Figure 3: (a,b) Process of identifying spring locations in the field.  
Figure 4: _Cont._