## 4 Methods
Each node in the tree refers to one of the features, and each leaf is assigned to one class (productive/non-productive area) representing the most frequent class value. Additionally, the leaf holds a probability vector indicating the probability of the feature's indicating a groundwater-productive area. New points are classified by navigating from the root of the tree to a leaf according to the outcome of tests along the path [27]. Figure 4 shows the general structure of a decision tree, which consists of three elements: node, condition, and production. Moreover, there are three types of node, namely decision, chance, and end nodes. In Figure 4, A is a decision node, B is chance (internal) nodes, and C is an end (leaf or terminal) node. Because of its advantages, the decision tree is a popular classification model. There are many algorithms for constructing decision tree models, such as the Classification and Regression Tree (CART) [28], the CHAID [29], the Iterative Dichotomiser 3 (ID3) [30], the QUEST [7], and the C4.5 algorithm [31]. In this study, the CHAID and QUEST decision tree algorithms were applied to GPP mapping. A CHAID decision tree is constructed by repeatedly splitting subsets of the space into two or more child nodes (multiway split), beginning with the entire dataset [29, 32]. The CHAID algorithm, which is applied widely in many fields, uses a recursive partitioning method and allows multiple splits of a node [33]. The CHAID algorithm consists of three steps: merging, splitting, and stopping. To determine the best split at any node, any allowable pair of categories of the predictor variables is merged until there is no statistically significant difference within the pair with respect to the target variable. The final nodes identify subgroups defined by different sets of independent variables. The result of these processes is a decision tree structure. The QUEST algorithm developed by [PERSON] and [PERSON] (1997) is a quick, unbiased, and efficient statistical tree. It is a tree-structured classification algorithm that yields a growing binary decision tree. The QUEST tree-growing process consists of selecting a split predictor, selecting a split point for the selected predictor, and stopping. The QUEST algorithm uses an unbiased or linear variable selection technique and employs imputation instead of surrogate splits to deal with missing values [34]. It can easily handle categorical predictor variables with many categories [7]. To apply the CHAID algorithm to develop decision trees, all the continuous factors except geology, hydrogeology, and soil were reclassified into 10 classes using frequency ratio values. The frequency ratio is the ratio of the area where SPC data existed to the total area. Factors were initially reclassified into 10 classes, each with a similar number of cells in total area. Thus, the range of each class was determined automatically based on equal area. Reclassification is a generalization technique used to reassign values, such as the ratio from the frequency ratio model, in an input theme to create a new input theme. Using the frequency ratio values, the 10 classes were then reclassified into three classes for applying the CHAID and QUEST algorithms. SPSS software [35] was used to implement the CHAID and QUEST algorithms. At the first step of the decision tree analysis, the frequency ratio data of factors, which were saved as ASCII files, were converted and loaded into SPSS data files. The SPSS decision trees algorithm offers a pruning mechanism in the tree induction stage for controlling the growth of the tree. At the next step, the values of the output attribute denoting groundwater productivity potential were listed via a model analysis tool. Finally, the observed and predicted values were saved in an ASCII file. An example of a decision tree derived from the CHAID algorithm is presented in Figure 4.