## 6 Conclusions and Discussion  
This study used the decision tree model to predict GPP areas in the Boryeong and Pohang cites, where groundwater production is expected to continue into the future. Two tree-constructing algorithms, CHAID and QUEST, were applied to map GPP and the GPP maps were validated. Generally, with respect to spatial distribution, the maps produced using the decision tree (CHAID and QUEST algorithms) approaches showed similar patterns, but patterns in some areas were different. The high potential areas are lowlands and either residential or agricultural areas. These areas of high and very high potential should be a priority concern for new development sites during groundwater development planning. The validation results showed satisfactory agreement between the GPP map and the existing well location data. In Boryeong city, the map produced using the CHAID and QUEST algorithms had high accuracies of 83.31% and 79.47% by the AUC method. In Pohang city, the accuracies were 86.18% (CHAID) and 80.00% (QUEST). The other validation method by comparing the GPP map with borehole SPC data showed accuracies of 96.55% and 94.92% (CHAID and QUEST) in Boryeong city and 87.88% and 87.50% (CHAID and QUEST) in Pohang city. The prediction accuracy obtained that the method applied for this study produced very reliable and accurate results. Generally, the CHAID algorithm showed better accuracy than the QUEST algorithm. The decision tree model can be used efficiently for GPP analysis and may be used widely for prediction of various spatial events. However, for generalizing the approach, more case studies including validations should be performed. For this, a hydrogeological database covering many areas should be prepared and the decision tree model should be applied to create a GPP map. To improve accuracy, not only applications and validation models but also accurate (large scale) and various input data should be considered.  
The main advantage of classification trees is the insight that their split points are believed to provide. [PERSON] and [PERSON] (1997) demonstrated that an exhaustive search tends to result in the selection of variables that afford more splits. Therefore, such trees should be interpreted with caution. In terms of classification accuracy, the variability in split points, and tree size, there is no clear best choice among most algorithms when univariate splits are used. However, the QUEST algorithm based on linear combination splits is usually shorter and more accurate than the same trees based on univariate splits [7]. 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].  
The model proposed in this paper and the resultant GPP maps can be applied to the establishment of development and management plans for the use of groundwater resources, such as for regional groundwater-development planning, decisions about promising areas for groundwater development, and control over the water supply system, based on a systematic, objective, and scientific decision model. The resulting maps also can help planners choose locations suitable for implementing further detailed explorations. Moreover, the use of the same analysis in other districts with similar topographic and geological conditions could result in time and cost savings in predicting groundwater productivity. If applied in other areas, the GPP mapping method developed in this study could become a widespread and useful analysis model for groundwater productivity. However, care should be taken in using the model for the development of specific sites as the scale of the analysis should be considered. Therefore, the model used in the study is valid only for generalized planning and assessment purposes.