Groundwater Potential Determination on Yasouj-Sisakht area Using Random Forest and Generalized Linear Statistical Models

Increasing population and agricultural development need dramatically water resources groundwater resources, therefore, are increasingly being considered, especially in arid and semi-arid regions. Aim of this research is mapping potential of groundwater resources on Yasouj-Sisakht region using data mining method Random Forest (RF) and Generalized Linear Statistical Model (GLM). For this purpose. For this purpose, information layers including slope, slope direction, slope length, aspect, topographic wetness index (TWI), distance from fault, distance from the stream, rainfall, land use, lithology, topographic position index (TPI) and stream power index (SPI) as the main factors influencing groundwater potential were identified and developed in ArcGIS and SAGAGIS software. From the distribution of 263 springs in the area, 70% (253 springs) were used as educational springs and 30% (109 springs) were used as experimental springs. The results showed that the level of underground water with low, medium, high and very high potential in the map of the random forest was 37.78, 22.22, 18.89 and 21.11%, respectively, and in the generalization linear model were 14.49, 32.04, 31.11 and 22.36%, respectively. Moreover, Sensitivity Analysis show that the factors affecting both methods are rainfall, altitude and distance from the fault factors. The accuracy of the data mining models used in this research was also evaluated using a relative performance curve (ROC). The area under curve (AUC) for both RF and GLM models is 92% and 65%, respectively. The accuracy of RF model, therefore, mapping groundwater potential in the study area is more than GLM model.