## 4 Methods  
The general progression of GPP mapping is illustrated in Figure 1. The groundwater potential related factors were used as the input data. The GPP-likely locations and the GPP-unlikely locations were selected as training sites. In the decision tree model, the selection of the training site is important, and the areas deemed likely and not likely were carefully considered for training in this study. So, to select the training sites based on scientific and objective criteria, the SPC criterion 4.55 m\({}^{3}\)/day/m (Boryeong) and 6.25 m\({}^{3}\)/day/m (Pohang) were used. In the Boryeong city, for example, the average yield of well data is 225 m\({}^{3}\)/day, mean depth of wells is 114 m and 10 m water table from ground. The SPC = 4.55 m\({}^{3}\)/day/m was taken as the criterion for division purposes. The criteria, 4.55 m\({}^{3}\)/day/m, are equivalent to well yield 300 m\({}^{3}\)/day, with supposed drawdown of 2/3 of the total well depth. The value was calculated from the relationship between mean depth of wells and yield SPC = yield/{2/3(mean depth of wells)-(water table from ground)}. The mean SPC was 11.46 m\({}^{3}\)/day/m (median 5.54 m\({}^{3}\)/day/m) with a maximum of 393.33 m\({}^{3}\)/day/m and a minimum of 1.08 m\({}^{3}\)/day/m. The SPC data that were not fewer than 4.55 m\({}^{3}\)/day/m were classified as GPP-likely training dataset, and the SPC data less than 4.55 m\({}^{3}\)/day/m were classified as GPP-unlikely dataset. Then, 50% of the SPC values that were \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Category**} & \multicolumn{3}{c}{**Factors**} & \multirow{2}{*}{**Data Type**} & **Scale** \\ \cline{2-2} \cline{4-4}  & **Boryeong City** & & **Pohang City** & \\ \hline River a & River Density & River Density & \\  & Distance from River & Distance from River & Line & 1:5000 \\ \hline Geology b & Geology Bedrock & & \\  & Hydrogeology & & \\ \hline  & Lineament Length Density & & \\  & Lineament Frequency & & \\  & Density & & \\  & Lineament Length Density & & \\  & Lineament Length Density & & \\  & Weighted by its Length & & \\  & Lineament Frequency & & \\  & Density Weighted by its & & \\  & Density Weighted by its & & \\  & Length & & \\  & Density for Lineament & & \\  & Cross Points & & \\ \hline Soil d & Soil & Soil & Polygon & R:30 m \\ \hline Forest e & Forest Density & - & Polygon & 1:25,000 \\ \hline  & Groundwater & Groundwater Depth & & \\  & Groundwater Gradient & - & Point \\ \hline \end{tabular} a Topographical factors were extracted from digital topographic map by National Geographic Information Institute [20]; b The geological map produced by the Korea Institute of Geoscience & Mineral Resource [21]; c The lineament factors were extracted from Landsat TM image of study area; d The detailed soil map produced by Rural Development Administration [22]; e The forest map produced by Korea Forest Service [23].  
\end{table} 
Table 2: _Cont._classified as GPP-likely training dataset were randomly selected and used for training. The remaining 50% of the GPP-likely dataset were used for validation. Using decision-tree model, the relationships between GPP-likely training dataset and each factor were calculated quantitatively and GPP maps were created based on these relationships. The decision trees were constructed using the CHAID and the QUEST algorithms. Because the QUEST algorithm supports only nominal categorical data, related factors used for analysis of GPP were classified by the value of the frequency ratio. The decision tree is a hierarchical model composed of decision rules that recursively splits independent variables into homogeneous zones [24, 25, 26]. It is a decision support method that uses a tree-like model of decisions and their possible consequences. The decision tree combines the features in a hierarchical fashion such that the most important feature is located at the root of the tree.