Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping

• Published in: Hydrogeology journal Vol. 27; no. 7; pp. 2511 - 2534
• Main Authors: Termeh, Seyed Vahid Razavi, Khosravi, Khabat, Sartaj, Majid, Keesstra, Saskia Deborah, Tsai, Frank T.-C., Dijksma, Roel, Pham, Binh Thai
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-11-2019; Springer Nature B.V
• Subjects: Adaptive systems; Algorithms; Aquatic Pollution; Artificial neural networks; Biogeography; Bivariate analysis; Cartography; Computer simulation; Decision making; Earth and Environmental Science; Earth Sciences; Entropy; Fuzzy logic; Fuzzy systems; Gene mapping; Genetic algorithms; Geology; Geophysics/Geodesy; Groundwater; Groundwater potential; Groundwater resources; Heuristic methods; Hydrogeology; Hydrology/Water Resources; Inference; Land use; Mapping; Model accuracy; Optimization; Optimization algorithms; Rain; Rainfall; Receptors; Resource management; Root-mean-square errors; Simulated annealing; Soil; Training; Waste Water Technology; Water Management; Water Pollution Control; Water Quality/Water Pollution; Water resources; Water wells; Wells; Iran; Groundwater management; Groundwater potential mapping; Bivariate models; Iran; Optimization
• ISSN: 1431-2174; 1435-0157
• DOI: 10.1007/s10040-019-02017-9

The main goal of this study was to optimize an adaptive neuro-fuzzy inference system (ANFIS) using three meta-heuristic optimization algorithms—genetic algorithm (GA), biogeography-based optimization (BBO) and simulated annealing (SA)—to prepare groundwater potential maps. The methodology was applied to the Booshehr plain, Iran. The results of optimized models were compared with ANFIS individually and three bivariate models: frequency ratio (FR), evidential belief function (EBF), and the entropy model. First, 339 wells with groundwater yield higher than 11 m
3
/h were selected and randomly divided into two groups. In all, 238 wells (70%) were used for training the models and 101 wells (30%) were used for testing and validating the models. Fifteen conditioning factors were selected as input parameters for the modeling. The accuracy of the groundwater potential maps for the study area was determined using root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and standard deviation of error (SD), as well as the area under the receiver operating characteristic (ROC) curve (AUC). Overall, the results demonstrated that ANFIS-GA had the highest prediction capability (AUC = 0.915) for groundwater potential mapping followed by ANFIS-BBO (0.903), entropy (0.862), FR (0.86), ANFIS-SA (0.83), ANFIS (0.82) and EBF (0.80). According to the entropy model, land-use, soil order and rainfall factors had the highest impact on groundwater potential in the study area. The results of this research show that the ANFIS models combined with meta-heuristic optimization algorithms can be a useful decision-making tool for assessment and management of groundwater resources.