干旱区生态环境知识资源中心(Arid): Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran)

Arid

DOI	10.3390/rs12030490
	Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran)
	Arabameri, Alireza 1; Lee, Saro 2,3; Tiefenbacher, John P.4; Phuong Thao Thi Ngo 5
通讯作者	Lee, Saro ; Phuong Thao Thi Ngo
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:3
英文摘要	The aim of this research is to introduce a novel ensemble approach using Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), frequency ratio (FR), and random forest (RF) models for groundwater-potential mapping (GWPM) in Bastam watershed, Iran. This region suffers from freshwater shortages and the identification of new groundwater sites is a critical need. Remote sensing and geographic information system (GIS) were used to reduce time and financial costs of rapid assessment of groundwater resources. Seventeen physiographical, hydrological, and geological groundwater conditioning factors (GWCFs) were derived from a spatial geo-database. Groundwater data were gathered in field surveys and well-yield data were acquired from the Iranian Department of Water Resources Management for 89 locations with high yield potential values >= 11 m(3) h(-1). These data were mapped in a GIS. From these locations, 62 (70%) were randomly selected to be used for model training, and the remaining 27 (30%) were used for validation of the model. The relative weights of the GWCFs were determined with an RF model. For GWPM, 220 randomly selected points in the study area and their final weights were determined with the VIKOR model. A groundwater potential map was created by interpolating the values at these points using Kriging in GIS. Finally, the area under receiver operating characteristic (AUROC) curve was plotted for the groundwater potential map. The success rate curve (SRC) was computed for the training dataset, and the prediction rate curve (PRC) was calculated for the validation dataset. Results of RF analysis show that land use and land cover, lithology, and elevation are the most significant determinants of groundwater occurrence. The validation results show that the ensemble model had excellent prediction performance (PRC = 0.934) and goodness-of-fit (SRC = 0.925) and reasonably high classification accuracy. The results of this study could aid management of groundwater resources and assist planners and decision makers in groundwater-investment planning to achieve sustainability.
英文关键词	modeling random forest frequency ration VIKOR model Bastam watershed
类型	Article
语种	英语
国家	Iran ; South Korea ; USA ; Vietnam
开放获取类型	gold, Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000515393800149
WOS关键词	MULTICRITERIA DECISION-MAKING ; EVIDENTIAL BELIEF FUNCTION ; BOOSTED REGRESSION TREE ; RANDOM FOREST MODELS ; REMOTE-SENSING DATA ; WEIGHTS-OF-EVIDENCE ; LOGISTIC-REGRESSION ; FREQUENCY RATIO ; INFORMATION-SYSTEM ; SPATIAL PREDICTION
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315421
作者单位	1.Tarbiat Modares Univ, Dept Geomorphol, Tehran 3658117994, Iran; 2.Korea Inst Geosci & Mineral Resources KIGAM, Geosci Platform Res Div, 124 Gwahak Ro, Daejeon 34132, South Korea; 3.Korea Univ Sci & Technol, 217 Gajeong Ro, Daejeon 34113, South Korea; 4.Texas State Univ, Dept Geog, San Marcos, TX 78666 USA; 5.Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
推荐引用方式 GB/T 7714	Arabameri, Alireza,Lee, Saro,Tiefenbacher, John P.,et al. Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran)[J],2020,12(3).
APA	Arabameri, Alireza,Lee, Saro,Tiefenbacher, John P.,&Phuong Thao Thi Ngo.(2020).Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran).REMOTE SENSING,12(3).
MLA	Arabameri, Alireza,et al."Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran)".REMOTE SENSING 12.3(2020).