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Machine learning based fast multi-layer liquefaction disaster assessment

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Abstract

Liquefaction is one kind of earthquake-induced disasters which may cause severe damages to roads, highways and buildings and consequently delay the disaster rescue and relief actions. A fast and reliable assessment of liquefaction disaster is thus of great importance for making disaster prevention plans beforehand and for planing rescue and relief activities right after earthquakes. However, this is still a great challenge task, because the computational cost of current existing liquefaction assessment methods is very high. For example, a 50 seconds simulation (5000 time steps) needs one hour with 1000 nodes in the Supercomputer K. In this paper, we proposed a machine learning based liquefaction disaster assessment method. Here, the assessment result can be given with high efficiency (few seconds or less) for emergency evacuation in an earthquake. Meanwhile, a multi-layer approach was also proposed. Firstly, the most dangerous area will be shown immediately by using convolutional neural network (CNN) model; followed by a high precision result, which is obtained by using fast Fourier transform and a special of soil (N values) coupled with a Light Gradient Boosting Machine (Light GBM) model. One more contribution is our visualization design, which can be used to let users know the dangerous area more intuitively. Finally, the effectiveness of our proposed method was demonstrated by assessing liquefaction from a large-scale earthquake simulation.

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  1. http://www.strata.jp/KobeJibankun/

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Acknowledgements

We would like to thank Professor Takeyama Tomohide for his suggestion in liquefaction disaster assessment, and anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. School of Computer Software, Tianjin University, Tianjin, 300350, China

    Chongke Bi, Bairan Fu & Yudong Zhao

  2. RIKEN Center for Computational Science, Kobe, Hyogo, 650-0047, Japan

    Jian Chen

  3. School of Mechanical Engineering, Tianjin University of Technology, Tianjin, 300384, China

    Lu Yang

  4. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Yulin Duan & Yun Shi

Authors
  1. Chongke Bi
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Correspondence to Jian Chen or Yulin Duan.

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This article belongs to the Topical Collection: Special Issue on Big Data for Effective Disaster Management

Guest Editors: Xuan Song, Song Guo, and Haizhong Wang

This work was partly supported by the National Natural Science Foundation of China under Grant No. 61702360, partly by the Tianjin Natural Science Foundation of China under Granted No. 16JCQNJC04100, partly supported by the FOCUS Establishing Supercomputing Center of Excellence and by the Council for Science, Technology and Innovation(CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), “Enhancement of societal resiliency against natural disasters” (Funding agency: JST). This research used the computational resources of the K computer provided by RIKEN Center for Computational Science.

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Bi, C., Fu, B., Chen, J. et al. Machine learning based fast multi-layer liquefaction disaster assessment. World Wide Web 22, 1935–1950 (2019). https://doi.org/10.1007/s11280-018-0632-8

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