Determination of Groundwater Potential Using Artificial Neural Network, Random Forest, Support Vector Machine and Linear Regression Models (Case Study: Lake Urmia Watershed)

The purpose of this study is to determine the areas with groundwater potential using artificial neural network (ANN), random forest (RF), support vector machine (SVM) and linear regression (GLM) models. In the present study, 14 parameters groundwater potential including altitude, slope, slope direction, curvature, distance to stream and fault, stream and fault density, lithology, average rainfall, land use, topographic position index (TPI), relative slope position (RSP) and topographic wetness index (TWI) were used. From a total of 10,624 springs, randomly 70% as test data and 30% as validation data were classified. The RF model was also used to determine the most important parameters. Alignment test between parameters was performed using SPSS software. The Receiver operating characteristic was used to Predictive power of models and the Seed Cell Area Indexes (SCAI) was used to accurately distinguish between classes. The results showed that there is no alignment between the parameters. The results of RF model showed that the parameters of height, land use, slope, and distance from fault, TWI and lithology are the most important factors affecting groundwater potential, respectively. Also, based on the ROC curve in both training (0.915) and validation (0.909), the ANN model had the highest accuracy and the RF, SVM and GLM models were in the next categories. Also, the results of the seed cell area index showed that all four models have separated the classes with appropriate accuracy. According to the ANN model, 31.4% of the basin has high and very high groundwater potential.