气候变化领域集成服务门户(CCPortal): Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization

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DOI	10.5194/hess-22-4771-2018
	Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization
	Khosravi K.; Panahi M.; Tien Bui D.
发表日期	2018
ISSN	1027-5606
起始页码	4771
结束页码	4792
卷号	22 期号:9
英文摘要	Groundwater is one of the most valuable natural resources in the world (Jha et al., 2007). However, it is not an unlimited resource; therefore understanding groundwater potential is crucial to ensure its sustainable use. The aim of the current study is to propose and verify new artificial intelligence methods for the spatial prediction of groundwater spring potential mapping at the Koohdasht-Nourabad plain, Lorestan province, Iran. These methods are new hybrids of an adaptive neuro-fuzzy inference system (ANFIS) and five metaheuristic algorithms, namely invasive weed optimization (IWO), differential evolution (DE), firefly algorithm (FA), particle swarm optimization (PSO), and the bees algorithm (BA). A total of 2463 spring locations were identified and collected, and then divided randomly into two subsets: 70% (1725 locations) were used for training models and the remaining 30% (738 spring locations) were utilized for evaluating the models. A total of 13 groundwater conditioning factors were prepared for modeling, namely the slope degree, slope aspect, altitude, plan curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distance from fault, distance from river, land use/land cover, rainfall, soil order, and lithology. In the next step, the step-wise assessment ratio analysis (SWARA) method was applied to quantify the degree of relevance of these groundwater conditioning factors. The global performance of these derived models was assessed using the area under the curve (AUC). In addition, the Friedman and Wilcoxon signed-rank tests were carried out to check and confirm the best model to use in this study. The result showed that all models have a high prediction performance; however, the ANFIS-DE model has the highest prediction capability (AUC Combining double low line 0.875), followed by the ANFIS-IWO model, the ANFIS-FA model (0.873), the ANFIS-PSO model (0.865), and the ANFIS-BA model (0.839). The results of this research can be useful for decision makers responsible for the sustainable management of groundwater resources. © Author(s) 2018.
语种	英语
scopus关键词	Decision making; Forecasting; Fuzzy neural networks; Fuzzy systems; Groundwater; Groundwater resources; Inference engines; Land use; Lithology; Location; Mapping; Particle swarm optimization (PSO); Adaptive neuro fuzzy inference systems (ANFIS); Adaptive neuro-fuzzy inference system; Area Under the Curve (AUC); Artificial intelligence methods; Invasive weed optimization; Meta-heuristic optimizations; Topographic wetness index; Wilcoxon signed rank test; Fuzzy inference; algorithm; artificial intelligence; land cover; land use; optimization; prediction; roughness; spring (hydrology); terrain; water resource; weed; Iran; Lorestan; Apoidea
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159917
作者单位	Khosravi, K., Department of Watershed Management Engineering, Faculty of Natural Resources, Sari Agricultural Science and Natural Resources University, Sari, Iran; Panahi, M., Young Researchers and Elites Club, North Tehran Branch, Islamic Azad University, Tehran, Iran; Tien Bui, D., GIS Group, Department of Business and IT, University of South-Eastern Norway, Gullbringvegen 36, Bø i Telemark, 3800, Norway
推荐引用方式 GB/T 7714	Khosravi K.,Panahi M.,Tien Bui D.. Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization[J],2018,22(9).
APA	Khosravi K.,Panahi M.,&Tien Bui D..(2018).Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization.Hydrology and Earth System Sciences,22(9).
MLA	Khosravi K.,et al."Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization".Hydrology and Earth System Sciences 22.9(2018).