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Application of the Levenburg-Marquardt back propagation neural network approach for landslide risk assessments
Xiong, Junnan1,3; Sun, Ming2; Zhang, Hao1; Cheng, Weiming3; Yang, Yinghui1; Sun, Mingyuan1; Cao, Yifan1; Wang, Jiyan1
2019-03-25
Source PublicationNATURAL HAZARDS AND EARTH SYSTEM SCIENCES
ISSN1561-8633
Volume19Issue:3Pages:629-653
Corresponding AuthorXiong, Junnan(neu_xjn@163.com) ; Zhang, Hao(zhanghao412658@163.com)
AbstractLandslide disasters are one of the main risks involved with the operation of long-distance oil and gas pipelines. Because previously established disaster risk models are too subjective, this paper presents a quantitative model for regional risk assessment through an analysis of the patterns of historical landslide disasters along oil and gas pipelines. Using the Guangyuan section of the Lanzhou-Chengdu-Chongqing (LCC) long-distance multiproduct oil pipeline (82 km) in China as a case study, we successively carried out two independent assessments: a susceptibility assessment and a vulnerability assessment. We used an entropy weight method to establish a system for the vulnerability assessment, whereas a Levenberg-Marquardt back propagation (LM-BP) neural network model was used to conduct the susceptibility assessment. The risk assessment was carried out on the basis of two assessments. The first, the system of the vulnerability assessment, considered the pipeline position and the angle between the pipe and the landslide (pipeline laying environmental factors). We also used an interpolation theory to generate the standard sample matrix of the LM-BP neural network. Accordingly, a landslide susceptibility risk zoning map was obtained based on susceptibility and vulnerability assessment. The results show that about 70% of the slopes were in high-susceptibility areas with a comparatively high landslide possibility and that the southern section of the oil pipeline in the study area was in danger. These results can be used as a guide for preventing and reducing regional hazards, establishing safe routes for both existing and new pipelines, and safely operating pipelines in the Guangyuan area and other segments of the LCC oil pipeline.
DOI10.5194/nhess-19-629-2019
WOS KeywordHAZARD ASSESSMENT ; BURIED PIPELINE ; VULNERABILITY ; RELIABILITY ; SENSITIVITY ; PREDICTION ; AREA ; FLOW ; GIS
Indexed BySCI
Language英语
Funding ProjectStrategic Priority Research Program of Chinese Academy of Sciences[XDA20030302] ; IWHR (China Institute of Water Resources and Hydropower Research) National Mountain Flood Disaster Investigation Project[SHZH-IWHR-57] ; Scientific and Technological Innovation Team Project of Southwest Petroleum University[2017CXTD09] ; Open Topic of Digital Fujian Institute of Large Data for Natural Disaster Monitoring[NDMBD2018003]
Funding OrganizationStrategic Priority Research Program of Chinese Academy of Sciences ; IWHR (China Institute of Water Resources and Hydropower Research) National Mountain Flood Disaster Investigation Project ; Scientific and Technological Innovation Team Project of Southwest Petroleum University ; Open Topic of Digital Fujian Institute of Large Data for Natural Disaster Monitoring
WOS Research AreaGeology ; Meteorology & Atmospheric Sciences ; Water Resources
WOS SubjectGeosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences ; Water Resources
WOS IDWOS:000462353400001
PublisherCOPERNICUS GESELLSCHAFT MBH
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/48668
Collection中国科学院地理科学与资源研究所
Corresponding AuthorXiong, Junnan; Zhang, Hao
Affiliation1.Southwest Petr Univ, Sch Civil Engn & Architecture, Chengdu 610500, Sichuan, Peoples R China
2.First Surveying & Mapping Engn Inst Sichuan Prov, Chengdu 610100, Sichuan, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Xiong, Junnan,Sun, Ming,Zhang, Hao,et al. Application of the Levenburg-Marquardt back propagation neural network approach for landslide risk assessments[J]. NATURAL HAZARDS AND EARTH SYSTEM SCIENCES,2019,19(3):629-653.
APA Xiong, Junnan.,Sun, Ming.,Zhang, Hao.,Cheng, Weiming.,Yang, Yinghui.,...&Wang, Jiyan.(2019).Application of the Levenburg-Marquardt back propagation neural network approach for landslide risk assessments.NATURAL HAZARDS AND EARTH SYSTEM SCIENCES,19(3),629-653.
MLA Xiong, Junnan,et al."Application of the Levenburg-Marquardt back propagation neural network approach for landslide risk assessments".NATURAL HAZARDS AND EARTH SYSTEM SCIENCES 19.3(2019):629-653.
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