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10-08-2019 | Paper

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

Authors: Seyed Vahid Razavi Termeh, Khabat Khosravi, Majid Sartaj, Saskia Deborah Keesstra, Frank T.-C. Tsai, Roel Dijksma, Binh Thai Pham

Published in: Hydrogeology Journal | Issue 7/2019

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Abstract

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 m³/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.

previous article Performance of the Gravity Recovery and Climate Experiment (GRACE) method in monitoring groundwater-level changes in local-scale study regions within Iran

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Title: Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping
Authors: Seyed Vahid Razavi Termeh
Khabat Khosravi
Majid Sartaj
Saskia Deborah Keesstra
Frank T.-C. Tsai
Roel Dijksma
Binh Thai Pham
Publication date: 10-08-2019
Publisher: Springer Berlin Heidelberg
Published in: Hydrogeology Journal / Issue 7/2019
Print ISSN: 1431-2174
Electronic ISSN: 1435-0157
DOI: https://doi.org/10.1007/s10040-019-02017-9

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