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Volume 35 Issue 1
Feb 2024
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Yongwei Li, Linrong Xu, Yonghui Shang, Shuyang Chen. Debris Flow Susceptibility Evaluation in Meizoseismal Region: A Case Study in Jiuzhaigou, China. Journal of Earth Science, 2024, 35(1): 263-279. doi: 10.1007/s12583-022-1803-1
Citation: Yongwei Li, Linrong Xu, Yonghui Shang, Shuyang Chen. Debris Flow Susceptibility Evaluation in Meizoseismal Region: A Case Study in Jiuzhaigou, China. Journal of Earth Science, 2024, 35(1): 263-279. doi: 10.1007/s12583-022-1803-1

Debris Flow Susceptibility Evaluation in Meizoseismal Region: A Case Study in Jiuzhaigou, China

doi: 10.1007/s12583-022-1803-1
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  • Corresponding author: Linrong Xu, lrxu@csu.edu.cn
  • Received Date: 24 Sep 2022
  • Accepted Date: 07 Dec 2022
  • Available Online: 01 Mar 2024
  • Issue Publish Date: 29 Feb 2024
  • Jiuzhaigou is situated on a mountain-canyon region and is famous for frequent tectonic activities. An abundance of loose co-seismic landslides and collapses were produced on gullies after the Jiuzhaigou Earthquake on August 8, 2017, which was served as material source for debris flow in later years. Debris flow appears frequently which are seriously endangering the safety of people's lives and properties. Even the earliest debris flow appeared in areas where no case ever reported before. The debris flow susceptibility evaluation (DFSE) is used for predicting the areas prone to debris flow, which is urgently required to avoid hazards and help to guide the strategy of preventive measures. Therefore, this work employs debris flow in Jiuzhaigou to reveal the characteristics of disaster-pregnant environment and to explore the application of machine learning in DFSE. Some new viewpoints are suggested: (ⅰ) Material density factor of debris flow is first adopted in this work, and it is proved to be a critical factor for triggering debris flows by sensitivity analysis method. (ⅱ) Deep neural network and convolutional neural network (CNN) achieve relatively good area under the curve (AUC) values and are 0.021–0.024 higher than traditional machine learning methods. (ⅲ) Watershed units combined with CNN-based model can achieve more accurate, reliable and practical susceptibility map. This work provides an idea for prevention of debris flow in mountainous lands.

     

  • Conflict of Interest
    The authors declare that they have no conflict of interest.
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