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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 06 Dec 2018

Research article | 06 Dec 2018

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Application of the LM-BP neural network approach for landslide risk assessments

Junnan Xiong1,3, Ming Sun2, Hao Zhang1, Weiming Cheng3, Yinghui Yang1, Mingyuan Sun1, Yifan Cao1, and Jiyan Wang1 Junnan Xiong et al.
  • 1School of Civil Engineering and Architecture, Southwest Petroleum University, Chengdu, 610500, P. R. China
  • 2Geodetic Third Team, National Administration of Surveying, Mapping and Geo-information of China, Chengdu, 610100, P. R. China
  • 3State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, P. R. China

Abstract. Landslide disaster is 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 laws of historical landslide disasters along oil and gas pipelines. Using the Guangyuan section of the Lanzhou–Chengdu–Chongqing (LCC) Long-Distance Products Oil Pipeline (82 km) in China as a case study, we successively carried out two independent assessments: a hazard 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 hazard 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 hazard risk zoning map was obtained based on hazard and vulnerability assessment. The results showed that about 70 % of the slopes were in high-hazard 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 section and other segments of the LCC oil pipeline.

Junnan Xiong et al.
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Junnan Xiong et al.
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Short summary
We want to know where prone to landslides or where pipeline is more unsafe. Through a model, We determined that there were 33.18 % and 40.46 % slopes of the total area in high-hazard and extremely high-hazard areas. the number and length of pipe segments in the highly vulnerable and extremely vulnerable area accounted for about 12 % of the total. In general, the pipeline risk within Qingchuan and Jian'ge Counties was relatively high.
We want to know where prone to landslides or where pipeline is more unsafe. Through a model, We...