The present research aims at applying three GIS-based bivariate models, namely, weights of evidence (WOE), weighting factor (WF), and statistical index (SI), for mapping of groundwater potential for sustainable groundwater management. The locations of wells with groundwater yields more than 11 m3/h were selected for modeling. Then, these locations were grouped into two categories with 70% (52 locations) in a training dataset to build the model and 30% (22 locations) in a testing dataset to validate it. Conditioning factors, namely, altitude, slope degree, plan curvature, slope aspect, rainfall, soil, land use, geology, distance from fault, and distance from river were selected. Finally, the three achieved maps were compared using area under receiver operating characteristic (ROC) and area under the ROC curve (AUC). The ROC method result showed that the SI model better fitted the training dataset (AUC = 0.747) followed by WF (AUC = 0.742) and WOE (AUC = 0.737). Results of the testing dataset show that the WOE model (AUC = 0.798) outperforms SI (AUC = 0.795) and WF (AUC = 0.791). According to the WF model, altitude and rainfall had the highest and lowest impacts on groundwater well potential occurrence, respectively. With regard to Friedman test, the difference in performances of these three models was not statistically significant.