期刊
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
卷 65, 期 16, 页码 2729-2749出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/02626667.2020.1828589
关键词
groundwater; adaptive neuro fuzzy inference system (ANFIS); metaheuristic approach; GIS; Navahad Plain; Iran
资金
- MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program [IITP-2020-2016-0-00312]
Due to climate change and urban growth, the demand for new freshwater sources, especially groundwater, is increasing in water-deficient countries like Iran. Therefore, this study aimed at groundwater potential mapping (GPM) of the Nahavand Plain, Iran, using an optimized adaptive neuro fuzzy inference system (ANFIS) in a geographic information system, with three metaheuristic optimization algorithms: differential evolution (DE), particle swarm optimization (PSO) and ant colony optimization (ACO). A spatial database was constructed using 273 spring locations and 14 groundwater conditioning factors. The optimization algorithms were evaluated using the receiver operating characteristic (ROC) technique. The ANFIS-DE, ANFIS-PSO and ANFIS-ACO models resulted in accuracy of 0.816, 0.809 and 0.758, respectively; the high and very high potential for groundwater springs covered 26% of the Nahavand Plain. The Root Mean Square Error (RMSE) for the training and validation datasets was lowest for the ANFIS-DE model compared to the other two models; and the ANFIS-PSO model had a higher convergence speed. These results may play an important role in sustainable groundwater management in the Nahavand Plain.
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