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DOI	10.1080/19475705.2021.1994024
	Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers
	Al-Bawi, Ahmed J.; Al-Abadi, Alaa M.; Pradhan, Biswajeet; Alamri, Abdullah M.
通讯作者	Al-Abadi, AM (corresponding author), Univ Basrah, Coll Sci, Dept Geol, Basrah, Iraq. ; Pradhan, B (corresponding author), Univ Technol Sydney, Fac Engn & IT, Ctr Adv Modelling & Geospatial Informat Syst, Sydney, NSW, Australia. ; Pradhan, B (corresponding author), Sejong Univ, Dept Energy & Mineral Resources Engn, Seoul, South Korea. ; Pradhan, B (corresponding author), Univ Kebangsaan Malaysia, Earth Observat Ctr, Inst Climate Change, Bangi, Selangor, Malaysia.
来源期刊	GEOMATICS NATURAL HAZARDS & RISK
ISSN	1947-5705
EISSN	1947-5713
出版年	2021
卷号	12 期号:1 页码:3035-3062
英文摘要	Gully erosion is an erosive process that contributes considerably to the shape of the earth's surface and is a major contributor to land degradation and soil loss. This study applied a methodology for mapping gully erosion susceptibility using only topographic related attributes derived from a medium-resolution digital elevation model (DEM) and a hybrid analytical hierarchy process (AHP) and the technique for an order of preference by similarity to ideal solutions (TOPSIS) and compare the results with naive Bayes (NB) and support vector machine learning (SVM) algorithms. A transboundary sub-basin in an arid area of southern Iraq was selected as a case study. The performance of the developed models was compared using the receiver operating characteristic curve (ROC). Results showed that the areas under the ROC were 0.933, 0.936, and 0.955 for AHP-TOPSIS, NB, and SVM with radial basis function, respectively, which indicated that the performance of simply derived AHP-TOPSIS model is similar to sophisticated NB and SVM models. Findings indicated that a medium resolution DEM and AHP-TOPSIS are a promising tool for mapping of gully erosion susceptibility.
英文关键词	Gully erosion gullies GIS remote sensing MCDM TOPSIS Iraq
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000710699400001
WOS关键词	HAZARD ASSESSMENT ; RANDOM FOREST ; MODELS ; REGION
WOS类目	Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences ; Water Resources
WOS研究方向	Geology ; Meteorology & Atmospheric Sciences ; Water Resources
来源机构	King Saud University
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368062
作者单位	[Al-Bawi, Ahmed J.; Al-Abadi, Alaa M.] Univ Basrah, Coll Sci, Dept Geol, Basrah, Iraq; [Pradhan, Biswajeet] Univ Technol Sydney, Fac Engn & IT, Ctr Adv Modelling & Geospatial Informat Syst, Sydney, NSW, Australia; [Pradhan, Biswajeet] Sejong Univ, Dept Energy & Mineral Resources Engn, Seoul, South Korea; [Pradhan, Biswajeet] Univ Kebangsaan Malaysia, Earth Observat Ctr, Inst Climate Change, Bangi, Selangor, Malaysia; [Alamri, Abdullah M.] King Saud Univ, Coll Sci, Dept Geol & Geophys, Riyadh, Saudi Arabia
推荐引用方式 GB/T 7714	Al-Bawi, Ahmed J.,Al-Abadi, Alaa M.,Pradhan, Biswajeet,et al. Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers[J]. King Saud University,2021,12(1):3035-3062.
APA	Al-Bawi, Ahmed J.,Al-Abadi, Alaa M.,Pradhan, Biswajeet,&Alamri, Abdullah M..(2021).Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers.GEOMATICS NATURAL HAZARDS & RISK,12(1),3035-3062.
MLA	Al-Bawi, Ahmed J.,et al."Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers".GEOMATICS NATURAL HAZARDS & RISK 12.1(2021):3035-3062.