DOI: 10.5194/hess-22-4771-2018
论文题名: Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization
作者: Khosravi K. ; Panahi M. ; Tien Bui D.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2018
卷: 22, 期: 9 起始页码: 4771
结束页码: 4792
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
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. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163200
Appears in Collections: 气候变化与战略
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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
Recommended Citation:
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]. Hydrology and Earth System Sciences,2018-01-01,22(9)