干旱区生态环境知识资源中心(Arid): Water Resources Management Through Flood Spreading Project Suitability Mapping Using Frequency Ratio, k-nearest Neighbours, and Random Forest Algorithms

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DOI	10.1007/s11053-019-09530-4
	Water Resources Management Through Flood Spreading Project Suitability Mapping Using Frequency Ratio, k-nearest Neighbours, and Random Forest Algorithms
	Naghibi, Seyed Amir 1; Vafakhah, Mehdi 1; Hashemi, Hossein 2; Pradhan, Biswajeet 3,4; Alavi, Seyed Jalil 5
通讯作者	Vafakhah, Mehdi
来源期刊	NATURAL RESOURCES RESEARCH
ISSN	1520-7439
EISSN	1573-8981
出版年	2020
卷号	29 期号:3 页码:1915-1933
英文摘要	Lack of water resources is a common issue in many countries, especially in the Middle East. Flood spreading project (FSP) is an artificial recharge technique, which is generally suggested for arid and semi-arid areas with two major aims including (1) flood mitigation and (2) artificial recharge of groundwater. This study implemented three state-of-the-art popular models including frequency ratio (FR), k-nearest neighbours (KNN), and random forest (RF) for determining the suitability of land for FSP. At the first step, suitable areas for FSP were identified according to the national guidelines and the literature. The identified areas were then verified by multiple field surveys. To produce FSP land suitability maps, several FSP conditioning factors such as topographical (i.e. slope, plan curvature, and profile curvature), hydrogeological (i.e. transmissivity, aquifer thickness, and electrical conductivity), hydrological (i.e. rainfall, distance from rivers, river density, and permeability), lithology, and land use were considered as input to the models. For the FR modelling, classified layers of the aforementioned variables were used, while their continuous layers were implemented in the KNN and RF algorithms. At the last step, receiver operating characteristic (ROC) curve was used to assess the ability and accuracy of the applied algorithms. Based on the findings, the area under the curve of ROC for the RF, KNN, and FR models was 97.1, 94.6, and 89.2%, respectively. Furthermore, transmissivity, slope, aquifer thickness, distance from rivers, rainfall, and electrical conductivity were recognized as the most influencing factors in the modelling procedure. The findings of this study indicated that the application of RF, KNN, and FR can be suggested for identification of suitable areas for FSP establishment in other regions.
英文关键词	Flood spreading project Artificial recharge Random forest Hydrogeology Data mining
类型	Article
语种	英语
国家	Iran ; Sweden ; Australia ; South Korea
收录类别	SCI-E
WOS记录号	WOS:000526461900025
WOS关键词	ARTIFICIAL GROUNDWATER RECHARGE ; BOOSTED REGRESSION TREE ; SUPPORT VECTOR MACHINE ; GAREH-BYGONE PLAIN ; LANDSLIDE SUSCEPTIBILITY ; LOGISTIC-REGRESSION ; GENETIC ALGORITHM ; NITRATE POLLUTION ; SPATIAL-ANALYSIS ; POTENTIAL SITES
WOS类目	Geosciences, Multidisciplinary
WOS研究方向	Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/318712
作者单位	1.Tarbiat Modares Univ, Fac Nat Resources, Dept Watershed Management Engn, Noor, Mazandaran, Iran; 2.Lund Univ, Ctr Middle Eastern Studies, Dept Water Resources Engn, Lund, Sweden; 3.Univ Technol Sydney, Fac Engn & IT, CAMGIS, Sydney, NSW 2007, Australia; 4.Sejong Univ, Dept Energy & Mineral Resources Engn, Seoul, South Korea; 5.Tarbiat Modares Univ, Fac Nat Resources, Dept Forestry, Noor, Mazandaran, Iran
推荐引用方式 GB/T 7714	Naghibi, Seyed Amir,Vafakhah, Mehdi,Hashemi, Hossein,et al. Water Resources Management Through Flood Spreading Project Suitability Mapping Using Frequency Ratio, k-nearest Neighbours, and Random Forest Algorithms[J],2020,29(3):1915-1933.
APA	Naghibi, Seyed Amir,Vafakhah, Mehdi,Hashemi, Hossein,Pradhan, Biswajeet,&Alavi, Seyed Jalil.(2020).Water Resources Management Through Flood Spreading Project Suitability Mapping Using Frequency Ratio, k-nearest Neighbours, and Random Forest Algorithms.NATURAL RESOURCES RESEARCH,29(3),1915-1933.
MLA	Naghibi, Seyed Amir,et al."Water Resources Management Through Flood Spreading Project Suitability Mapping Using Frequency Ratio, k-nearest Neighbours, and Random Forest Algorithms".NATURAL RESOURCES RESEARCH 29.3(2020):1915-1933.