Loess slide susceptibility assessment using frequency ratio model and artificial neural network

doi:10.1144/qjegh2017-056

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所 > 山地灾害与地表过程重点实验室

	Loess slide susceptibility assessment using frequency ratio model and artificial neural network
	Qiu, Haijun 1,2,3; Cui, Peng4 ; Regmi, Amar Deep 4; Hu, Sheng 3; Hao, Junqing 5
刊名	QUARTERLY JOURNAL OF ENGINEERING GEOLOGY AND HYDROGEOLOGY
	2019-02-01
卷号	52 期号:1 页码:38-45
ISSN号	1470-9236
DOI	10.1144/qjegh2017-056
通讯作者	Qiu, Haijun(13991345616@163.com)
英文摘要	Landslide susceptibility assessment is essential for disaster management. The aim of this study is to select a reliable and accurate model for loess slide susceptibility assessment. We use a frequency ratio model and artificial neural network to develop loess slide susceptibility maps. We analysed the relationships between loess slide frequency and conditioning factors including elevation, slope gradient, aspect, profile curvature, thickness of loess, rainfall, topographic wetness index, valley depth, distance to rivers and land use. We developed a landslide inventory consisting of 223 loess slides by the interpretation of remote sensing images from earlier published or unpublished reports and from intensive field surveys. From these 223 loess slides, 178 (80%) were selected for training the models and the remaining 45 (20%) slides were used for validating the developed models. The validation was carried out by using receiver operating characteristic (ROC) curves. From the analysis, it is seen that both the frequency ratio model and artificial neural network performed equally well, although the frequency ratio method is much easier to apply. The loess slide susceptibility maps can be used for land use planning and risk mitigation in loess terrain.
资助项目	International Partnership Program of Chinese Academy of Sciences[131551KYSB20160002] ; National Natural Science Foundation of China[41771539]
WOS关键词	LANDSLIDE SUSCEPTIBILITY ; DEBRIS-FLOW ; LANTAU-ISLAND ; AREA ; TERRAIN ; HAZARD ; SCALE ; GIS ; PLATEAU ; YENICE
WOS研究方向	Engineering ; Geology
语种	英语
出版者	GEOLOGICAL SOC PUBL HOUSE
WOS记录号	WOS:000457570500005
资助机构	International Partnership Program of Chinese Academy of Sciences ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.imde.ac.cn/handle/131551/25044]
专题	成都山地灾害与环境研究所_山地灾害与地表过程重点实验室
通讯作者	Qiu, Haijun
作者单位	1.Northwest Univ, Shaanxi Key Lab Earth Surface Syst & Environm Car, Xian 710127, Shaanxi, Peoples R China 2.Northwest Univ, Inst Earth Surface Syst & Hazards, Xian 710127, Shaanxi, Peoples R China 3.Northwest Univ, Coll Urban & Environm Sci, Xian 710127, Shaanxi, Peoples R China 4.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Sichuan, Peoples R China 5.Xian Univ Finance & Econ, Sch Business, Xian 710061, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Qiu, Haijun,Cui, Peng,Regmi, Amar Deep,et al. Loess slide susceptibility assessment using frequency ratio model and artificial neural network[J]. QUARTERLY JOURNAL OF ENGINEERING GEOLOGY AND HYDROGEOLOGY,2019,52(1):38-45.
APA	Qiu, Haijun,Cui, Peng,Regmi, Amar Deep,Hu, Sheng,&Hao, Junqing.(2019).Loess slide susceptibility assessment using frequency ratio model and artificial neural network.QUARTERLY JOURNAL OF ENGINEERING GEOLOGY AND HYDROGEOLOGY,52(1),38-45.
MLA	Qiu, Haijun,et al."Loess slide susceptibility assessment using frequency ratio model and artificial neural network".QUARTERLY JOURNAL OF ENGINEERING GEOLOGY AND HYDROGEOLOGY 52.1(2019):38-45.