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Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China

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Listed:
  • Chong Xu
  • Xiwei Xu
  • Fuchu Dai
  • Zhide Wu
  • Honglin He
  • Feng Shi
  • Xiyan Wu
  • Suning Xu

Abstract

The main purpose of this paper is to present the use of multi-resource remote sensing data, an incomplete landslide inventory, GIS technique and logistic regression model for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Landslide location polygons were delineated from visual interpretation of aerial photographs, satellite images in high resolutions, and verified by selecting field investigations. Eight factors, including slope angle, slope aspect, elevation, distance from drainages, distance from roads, distance from main faults, seismic intensity and lithology were selected as controlling factors for earthquake-triggered landslide susceptibility mapping. Qualitative susceptibility analyses were carried out using the map overlaying techniques in GIS platform. The validation result showed a success rate of 82.751 % between the susceptibility probability index map and the location of the initial landslide inventory. The predictive rate of 86.930 % was obtained by comparing the additional landslide polygons and the landslide susceptibility probability index map. Both the success rate and the predictive rate show sufficient agreement between the landslide susceptibility map and the existing landslide data, and good predictive power for spatial prediction of the earthquake-triggered landslides. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • Chong Xu & Xiwei Xu & Fuchu Dai & Zhide Wu & Honglin He & Feng Shi & Xiyan Wu & Suning Xu, 2013. "Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China," 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. 68(2), pages 883-900, September.
    • Handle: RePEc:spr:nathaz:v:68:y:2013:i:2:p:883-900
    DOI: 10.1007/s11069-013-0661-7
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    2. Guru Balamurugan & Veerappan Ramesh & Mangminlen Touthang, 2016. "Landslide susceptibility zonation mapping using frequency ratio and fuzzy gamma operator models in part of NH-39, Manipur, India," 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. 84(1), pages 465-488, October.
    3. Xin Wei & Lulu Zhang & Junyao Luo & Dongsheng Liu, 2021. "A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping," 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. 109(1), pages 471-497, October.
    4. Yashar Alimohammadlou & Burak F. Tanyu & Aiyoub Abbaspour & Paul L. Delamater, 2021. "Automated landslide detection model to delineate the extent of existing landslides," 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. 107(2), pages 1639-1656, June.

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