World Wide Web

, Volume 22, Issue 5, pp 1935–1950 | Cite as

Machine learning based fast multi-layer liquefaction disaster assessment

  • Chongke Bi
  • Bairan Fu
  • Jian ChenEmail author
  • Yudong Zhao
  • Lu Yang
  • Yulin DuanEmail author
  • Yun Shi
Part of the following topical collections:
  1. Special Issue on Big Data for Effective Disaster Management


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.


Liquefaction disaster assessment Machine learning Multi-layer 



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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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer SoftwareTianjin UniversityTianjinChina
  2. 2.RIKEN Center for Computational ScienceKobeJapan
  3. 3.School of Mechanical EngineeringTianjin University of TechnologyTianjinChina
  4. 4.Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijingChina

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