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Factor-based probability model for vulnerability assessment of slopes subjected to earthquakes

Author

Listed:
  • Meng-Hsun Hsieh

    (China University of Technology)

  • Jeng-Wen Lin

    (Feng Chia University)

  • Yu-Jen Li

    (Ruentex Engineering & Construction Co., Ltd.)

Abstract

This study explores the slope vulnerability assessment subjected to earthquakes using an environmental factor-based probability model based on data from the September 21, 1999, Chi-Chi earthquake-stricken areas of Taiwan. Satellite images were used to identify and interpret the collapsed area in the study area, and strong ground motion station data were used to evaluate the PGA. A DEM was used for topographic analysis to establish 6333 slope units. These slope units were classified into 12 categories based on environmental factors. Finally, a deterministic probability model based on the classification of environmental factors was presented. Landslide fragility functions were estimated for the 12 slope classifications. The results indicated that the important factors affecting the Chi-Chi earthquake were geology, aspect, and gradient. Geological factors indicate that the landslide probabilities of dense soil and soft rock are higher than those of hard and brittle rocks. The strong and weak aspects of the slope direction indicated that the difference in landslide probability was between about 0.05 and 0.1. The gradient factor indicates that steep slopes have a higher landslide probability.

Suggested Citation

  • Meng-Hsun Hsieh & Jeng-Wen Lin & Yu-Jen Li, 2025. "Factor-based probability model for vulnerability assessment of slopes subjected to earthquakes," 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. 121(5), pages 5781-5798, March.
    • Handle: RePEc:spr:nathaz:v:121:y:2025:i:5:d:10.1007_s11069-024-07029-7
    DOI: 10.1007/s11069-024-07029-7
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    References listed on IDEAS

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    1. Viet-Ha Nhu & Ayub Mohammadi & Himan Shahabi & Baharin Bin Ahmad & Nadhir Al-Ansari & Ataollah Shirzadi & John J. Clague & Abolfazl Jaafari & Wei Chen & Hoang Nguyen, 2020. "Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment," IJERPH, MDPI, vol. 17(14), pages 1-23, July.
    2. Helen Cristina Dias & Marcelo Fischer Gramani & Carlos Henrique Grohmann & Carlos Bateira & Bianca Carvalho Vieira, 2021. "Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast," 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. 108(1), pages 205-223, August.
    3. Tsu-Chiang Lei & Yi-Min Huang & Bing-Jean Lee & Meng-Hsun Hsieh & Kuan-Ting Lin, 2014. "Development of an empirical model for rainfall-induced hillside vulnerability assessment: a case study on Chen-Yu-Lan watershed, Nantou, Taiwan," 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. 74(2), pages 341-373, November.
    4. Kamila Hodasová & Martin Bednarik, 2021. "Effect of using various weighting methods in a process of 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. 105(1), pages 481-499, January.
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