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Improving groundwater potential mapping using metaheuristic approaches.
- Source :
-
Hydrological Sciences Journal/Journal des Sciences Hydrologiques . Dec2020, Vol. 65 Issue 16, p2729-2749. 21p. - Publication Year :
- 2020
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02626667
- Volume :
- 65
- Issue :
- 16
- Database :
- Academic Search Index
- Journal :
- Hydrological Sciences Journal/Journal des Sciences Hydrologiques
- Publication Type :
- Academic Journal
- Accession number :
- 147339044
- Full Text :
- https://doi.org/10.1080/02626667.2020.1828589