Novel hybrid models combining meta-heuristic algorithms with support vector regression (SVR) for groundwater potential mapping

Abstract

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.

Keywords:

• Groundwater potential mapping
• support vector regression
• evolutionary algorithms

Disclosure statement

No potential conflict of interest was reported by the author(s).

Table 3. The AUROC values for three EAs in tuning process.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.