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Hazard degree assessment of landslide using set pair analysis method

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  • Wang Wensheng
  • Li Yueqing

Abstract

It is a critical issue to assess landslide hazard degree in the risk analysis of mountain geo-hazards. Currently, different kinds of approaches to assess landslide hazard degree are developed and utilized from single quantitative analysis to integrated quantitative analysis. However, several problems of these approaches impede further applications. These problems include (1) the methods are complex and mathematic background is required sometimes; (2) the methods are hard to utilize. This study comes up with a new approach to assess landslide hazard degree: a set pair analysis method (SPAM). The SPAM takes the fuzzy property of grade standards into full account and avoids the values of discrepancy uncertainty coefficient in connection degree formula. A case study is presented to assess the landslide hazard degree of 24 typical landslides in Fengjie County, Chongqing City, where the Three Gorges Dam is located. The proposed method is simple in concept and easy in calculation and application. Its results are credible. Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • Wang Wensheng & Li Yueqing, 2012. "Hazard degree assessment of landslide using set pair analysis method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 60(2), pages 367-379, January.
  • Handle: RePEc:spr:nathaz:v:60:y:2012:i:2:p:367-379
    DOI: 10.1007/s11069-011-0017-0
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    References listed on IDEAS

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    1. A. Carrara & F. Guzzetti & M. Cardinali & P. Reichenbach, 1999. "Use of GIS Technology in the Prediction and Monitoring of Landslide Hazard," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 20(2), pages 117-135, November.
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