Articles | Volume 16, issue 17
https://doi.org/10.5194/gmd-16-5113-2023
https://doi.org/10.5194/gmd-16-5113-2023
Model evaluation paper
 | 
06 Sep 2023
Model evaluation paper |  | 06 Sep 2023

Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China

Xiaoyi Shao, Siyuan Ma, and Chong Xu

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Cited articles

Allstadt, K. E., Jibson, R. W., Thompson, E. M., Massey, C. I., Wald, D. J., Godt, J. W., and Rengers, F. K.: Improving near-real-time coseismic landslide models: Lessons learned from the 2016 Kaikōura, New Zealand, Earthquake, B. Seismol. Soc. Am., 108, 1649–1664, https://doi.org/10.1785/0120170297, 2018. 
Bai, S. B., Lu, P., and Wang, J.: Landslide susceptibility assessment of the Youfang Catchment using logistic regression, J. Mt. Sci., 816–827, https://doi.org/10.1007/s11629-014-3171-5, 2015. 
Bragagnolo, L., da Silva, R. V., and Grzybowski, J. M. V.: Landslide susceptibility mapping with r.landslide: A free open-source GIS-integrated tool based on Artificial Neural Networks, Environ. Modell. Softw., 123, 104565, https://doi.org/10.1016/j.envsoft.2019.104565, 2020. 
Brenning, A.: Spatial prediction models for landslide hazards: review, comparison and evaluation, Nat. Hazards Earth Syst. Sci., 5, 853–862, https://doi.org/10.5194/nhess-5-853-2005, 2005. 
Broeckx, J., Vanmaercke, M., Duchateau, R., and Poesen, J.: A data-based landslide susceptibility map of Africa, Earth-Sci. Rev., 185, 102–121, https://doi.org/10.1016/j.earscirev.2018.05.002, 2018. 
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Short summary
Scientific understandings of the distribution of coseismic landslides, followed by emergency and medium- and long-term risk assessment, can reduce landslide risk. The aim of this study is to propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software called Mat.LShazard V1.0, which provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages.