This study aims to develop three novel GIS-based models combining Genetic Algorithm (GA), Biogeography-Based Optimization (BBO) and Simulated Annealing (SA) with Support Vector Regression (SVR) for groundwater potential (GP) mapping in the governorate of Tafillah, Jordan. Twelve topographical, hydrological and geological factors were considered. The mapping process was done with and without feature selection (FS) conducted by integration of SVR model with GA, BBO and SA algorithms. The accuracy of these models was evaluated using the area under receiver operating characteristic (AUROC) curve. Comparisons among the models uncovered that the SVR-RBF-GA and SVR-RBF-BBO models performed better than the SVR-RBF-SA. The AUROC for two mentioned models were 0.964 and 0.996 in training and testing runs, respectively, while this metric was 0.953 and 0.986 for SVR-RBF-SA model in training and testing runs, respectively. The results showed that after FS, the models are more accurate in test data than train data.

The groundwater contained in aquifers is among the most important water supply resources, especially in semi-arid and arid regions worldwide. This study aims to evaluate and compare the prediction capability of two well–known models, support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS), combined with a genetic algorithm (GA), invasive weed optimization (IWO), and teaching–learning-based optimization (TLBO) algorithms in groundwater potential mapping (GPM) the Azraq Basin in Jordan. The hybridization of the SVM and ANFIS models with the GA, IWO, and TLBO algorithms results in six models: SVM–GA, SVM–IWO, SVM–TLBO, ANFIS–GA, ANFIS–IWO, and ANFIS–TLBO. A database consisting of well data containing 464 wells with 12 predictive factors was developed for the groundwater potential mapping (GPM) of the study area. Of the 464 well locations, 70% (325 locations) were assigned for the training set and the rest (139 locations) for the validation set. The correlation between the 12 predictive factors and the well locations is analyzed using the frequency ratio (FR) statistical model. An area under receiver operating characteristic (AUROC) curve was used to evaluate and compare the models. According to the results, the SVM-based hybrid models outperformed other ANFIS hybrid models in the learning (training) and validation phases. The SVM–GA and SVM–TLBO hybrid models showed AUROC values of 0.984 and 0.971, respectively, in the training and validation phases. Moreover, the ANFIS–GA and ANFIS–TLBO hybrid models showed an AUROC of 0.979 and 0.984 in the training phase and an AUROC of 0.973 and 0.984 in the validation phase, respectively. The SVM–IWO and ANFIS–IWO hybrid models showed the lowest AUROC. This study demonstrated the more efficient results of the SVM-based hybrid models in comparison with the ANFIS-based hybrid models in terms of accuracy and modeling speed.

In light of advancements in energy sources in remote areas of Jordan that occurred at the onset of the 21st century, it is crucial to explore how solar...