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Susceptibility mapping of landslides in Beichuan County using cluster and MLC methods

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  • Mingtao Ding
  • Kaiheng Hu

Abstract

Cluster analysis and maximum likelihood classification (MLC) are exploited to map the post-earthquake landslide susceptibility in Beichuan County that was affected by the Ms 8.0 Wenchuan earthquake. The methodology is applicable even if there is short of training data. Six effective factors are chosen for mapping the susceptibility, including land use, seismic intensity, average annual rainfall, relative relief, slop gradient and lithology. Four clusters are grouped from sampling grid cells by k-means clustering approach. MLC classifies all the cells in the study area into the four clusters according to their statistical characteristics. Four susceptibility classes (extreme low, low, moderate and high) are assigned to these clusters applying expert experience and hazard density. The final map gives a reasonable assessment of post-earthquake landslide susceptibility in Beichuan County. Comparing with the pre-earthquake susceptibility map made in Beichuan County geological disaster survey project, the result t using cluster and MLC classification has a better agreement with the dot density value of post-earthquake landslides in Beichuan County. The susceptibility map can be used to identify safety spots within the high danger area, which are suitable for habitations and facilities. It is also found that more landslides are densely concentrated at the boundary between high and moderate regions, and between high and extreme low regions. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Mingtao Ding & Kaiheng Hu, 2014. "Susceptibility mapping of landslides in Beichuan County using cluster and MLC methods," 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. 70(1), pages 755-766, January.
  • Handle: RePEc:spr:nathaz:v:70:y:2014:i:1:p:755-766
    DOI: 10.1007/s11069-013-0854-0
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    References listed on IDEAS

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    1. C. van Westen & N. Rengers & R. Soeters, 2003. "Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment," 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. 30(3), pages 399-419, November.
    2. Núria Santacana & Baeza Baeza & Jordi Corominas & Ana De Paz & Jordi Marturiá, 2003. "A GIS-Based Multivariate Statistical Analysis for Shallow Landslide Susceptibility Mapping in La Pobla de Lillet Area (Eastern Pyrenees, Spain)," 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. 30(3), pages 281-295, November.
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    Cited by:

    1. Deborah Simon Mwakapesa & Yimin Mao & Xiaoji Lan & Yaser Ahangari Nanehkaran, 2023. "Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance," Sustainability, MDPI, vol. 15(5), pages 1-20, February.
    2. Rui-Xuan Tang & E-Chuan Yan & Tao Wen & Xiao-Meng Yin & Wei Tang, 2021. "Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping," Sustainability, MDPI, vol. 13(7), pages 1-25, March.

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