| 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
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| Corresponding Author | Qiu, Haijun(13991345616@163.com) |
| 2019-02-01 | |
| Source Publication | QUARTERLY JOURNAL OF ENGINEERING GEOLOGY AND HYDROGEOLOGY
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| ISSN | 1470-9236 |
| Volume | 52Issue:1Pages:38-45 |
| Abstract | 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inventory consisting of 223 loess slides by the interpretation of remote sensing images from earlier published or unpublished reports and from intensive field surveys.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. |
| DOI | 10.1144/qjegh2017-056 |
| Indexed By | SCI |
| WOS Keyword | LANDSLIDE SUSCEPTIBILITY ; DEBRIS-FLOW ; LANTAU-ISLAND ; AREA ; TERRAIN ; HAZARD ; SCALE ; GIS ; PLATEAU ; YENICE |
| Language | 英语 |
| Funding Project | International Partnership Program of Chinese Academy of Sciences[131551KYSB20160002] ; National Natural Science Foundation of China[41771539] |
| WOS Research Area | Engineering ; Geology |
| WOS Subject | Engineering, Geological ; Geosciences, Multidisciplinary |
| WOS ID | WOS:000457570500005 |
| Funding Organization | International Partnership Program of Chinese Academy of Sciences ; National Natural Science Foundation of China |
| Publisher | GEOLOGICAL SOC PUBL HOUSE |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.imde.ac.cn/handle/131551/25045 |
| Collection | 山地灾害与地表过程重点实验室 |
| Corresponding Author | Qiu, Haijun |
| Affiliation | 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 |
| 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Loess slide suscepti（2159KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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