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Loess slide susceptibility assessment using frequency ratio model and artificial neural network
Qiu, Haijun1,2,3; Cui, Peng4; Regmi, Amar Deep4; Hu, Sheng3; Hao, Junqing5
Corresponding AuthorQiu, Haijun(13991345616@163.com)
2019-02-01
Source PublicationQUARTERLY JOURNAL OF ENGINEERING GEOLOGY AND HYDROGEOLOGY
ISSN1470-9236
Volume52Issue:1Pages:38-45
AbstractLandslide 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inventory consisting of 223 loess slides by the interpretation of remote sensing images from earlier published or unpublished reports and from intensive field surveys.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.
DOI10.1144/qjegh2017-056
Indexed BySCI
WOS KeywordLANDSLIDE SUSCEPTIBILITY ; DEBRIS-FLOW ; LANTAU-ISLAND ; AREA ; TERRAIN ; HAZARD ; SCALE ; GIS ; PLATEAU ; YENICE
Language英语
Funding ProjectInternational Partnership Program of Chinese Academy of Sciences[131551KYSB20160002] ; National Natural Science Foundation of China[41771539]
WOS Research AreaEngineering ; Geology
WOS SubjectEngineering, Geological ; Geosciences, Multidisciplinary
WOS IDWOS:000457570500005
Funding OrganizationInternational Partnership Program of Chinese Academy of Sciences ; National Natural Science Foundation of China
PublisherGEOLOGICAL SOC PUBL HOUSE
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/25045
Collection中国科学院水利部成都山地灾害与环境研究所
Corresponding AuthorQiu, Haijun
Affiliation1.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
Recommended Citation
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.
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