Regional landslide susceptibility zoning with considering the aggregation of landslide points and the weights of factors

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• 作者：Xueliang Wang (1)
  Luqing Zhang (1)
  Sijing Wang (1)
  Serena Lari (2)
  
• 关键词：Landslide susceptibility ; Class landslide susceptibility index ; Landslide point aggregation ; Uncertainty ; Validation
• 刊名：Landslides
• 出版年：2014
• 出版时间：June 2014
• 年：2014
• 卷：11
• 期：3
• 页码：399-409
• 全文大小：
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• 作者单位：Xueliang Wang (1)
  Luqing Zhang (1)
  Sijing Wang (1)
  Serena Lari (2)
  
  1. Key Laboratory of Engineering Geomechanics, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, 100029, China
  2. Dipartimento di Scienze Geologiche e Geotecnologie, Università degli Studi di Milano-Bicocca, Milano, Italy
  
• ISSN：1612-5118

文摘

In this paper, we propose a methodology for landslide susceptibility assessment at a regional scale in Yunnan, southwestern province of China. A landslide inventory map including 3,242 landslide points was prepared for the study area. Five factors recognized as correlated to landslide (namely, lithology, relative relief, tectonic fault density, rainfall, and road density) were analyzed and mapped in geographic information system. An index expressing the correlation between each factor and landslides [called class landslide susceptibility index (CLSI)] was proposed in the study. While analyzing landslide distribution in a large area, point aggregation might be expected. To quantify the uncertainty caused by aggregation, class landslide aggregation index was proposed. To account for the importance of each of the factors in the landslide susceptibility assessment, some weights were calculated by means of analytic hierarchy process. We propose a weighted class landslide susceptibility model (WCLSM), obtained by the combination of CLSI values of each factor with the correspondent weight. WCLSM performance in the study area was evaluated comparing the results obtained by first modeling all landslides and then by performing a time partition. The model was run including only landslides that occurred before 2009 and then validated with respect to landslides that occurred after 2009. The prediction–rate curve shows that the WCLSM model provides a good prediction for the study area. Of the study area, 21.4?% shows very high and high susceptibility and includes the 87.7?% of the number of landslides that occurred after 2009.