GIS-based groundwater potential mapping using artificial neural network and support vector machine models: the case of Boryeong city in Korea

Abstract Groundwater productivity-potential (GPP) was analysed using the data mining models of an artificial neural network (ANN) and a support vector machine (SVM) in Boryeong city, Korea. The groundwater-productivity data with specific capacity (SPC) is strongly related to hydrogeological factors, and hence the relation may allow for groundwater potential mapping from hydrogeological factors through the ANN and SVM models. A back-propagation algorithm was used for the ANN model while a polynomial kernel was adopted for the SVM model. For the validation of the GPP maps generated from the ANN and SVM models, the area-under-the-curve analysis was performed using the SPC values of well data. The accuracies achieved from the ANN and SVM models are about 83.57 and 80.83%, respectively. It proves that the ANN and SVM models will be highly conducive to developing useful groundwater resources.