Advertisement

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
Article
Part of the following topical collections:
  1. Special Issue on Big Data for Effective Disaster Management

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.

Keywords

Liquefaction disaster assessment Machine learning Multi-layer 

Notes

Acknowledgements

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

References

  1. 1.
    Bi, C., Yuan, Y., Zhang, J., Shi, Y., Xiang, Y., Wang, Y., Zhang, R.: Dynamic mode decomposition based video shot detection. IEEE Access 6, 21397 (2018)CrossRefGoogle Scholar
  2. 2.
    Bi, C., Yuan, Y., Zhang, R., Xiang, Y., Wang, Y., Zhang, J.: A dynamic mode decomposition based edge detection method for art images. IEEE Photon. J. 9 (6), 1 (2017)CrossRefGoogle Scholar
  3. 3.
    Biot, M.: Theory of propagation of elastic wave in a fluid-saturated porous solid. J Acoust Soc Am 28(2), 168 (1956)CrossRefGoogle Scholar
  4. 4.
    Chen, J., Takeyama, T., O-Tani, H., Fujita, K., Hori, M.: A framework for assessing liquefaction hazard for urban areas based on soil dynamics. Int. J. Comput. Method. 13(04), 1641011 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Chen, J., Takeyama, T., O-Tani, H., Fujita, K., Motoyama, H., Hori, M.: Using high performance computing for liquefaction hazard assessment with statistical soil models. Int. J. Comput. Method 15(2), 1840005 (2018)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Chen, Q., Song, X., Yamada, H., Shibasaki, R.: Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference. In: AAAI Conference on Artificial Intelligence, pp. 338–344 (2016)Google Scholar
  7. 7.
    Chen, T., Guestrin, C.: Liquefaction investigation of Wenchuan earthquake. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)Google Scholar
  8. 8.
    Cubrinovski, M., Henderson, D., Bradley, B.: Liquefaction impacts in residential areas in the 2010-2011 Christchurch earthquakes. In: Proceedings of the International Symposium on Engineering Lessons Learned from the 2011 Great East Japan Earthquake, pp. 811–824 (2012)Google Scholar
  9. 9.
    Cubrinovski, M., Ishihara, K., Pipatpongsa, T., Tanizawa, F.: Numerical simulation of the Kobe Port-Island liquefaction. In: Proceedings of the 11th World Conference on Earthquake Engineering, p 330 (1996)Google Scholar
  10. 10.
    Dhanya, J., Raghukanth, S.T.G.: Ground motion prediction model using artificial neural network. Pure Appl. Geophysics 175(3), 1035 (2018)CrossRefGoogle Scholar
  11. 11.
    Erzin, Y., Ecemis, N.: The use of neural networks for cpt-based liquefaction screening. Bull. Eng. Geol. Environ. 74(1), 103 (2015)CrossRefGoogle Scholar
  12. 12.
    Fang, J.T., Chang, Y.R., Chang, P.C.: Deep learning of chroma representation for cover song identification in compression domain. Multidim. Syst. Sign. Process. 29, 887 (2018)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Farid, D.M., Zhang, L., Rahman, C.M., Hossain, M.A., Strachan, R.: Hybrid decision tree and naïve bayes classifiers for multi-class classification tasks. Expert Syst. Appl. 41, 1937 (2014)CrossRefGoogle Scholar
  14. 14.
    Fujita, K., Ichimura, T., Hori, M., Wijerathne, M., Tanaka, S.: A quick earthquake disaster estimation system with fast urban earthquake simulation and interactive visualization. Procedia Comput. Sci. 29, 866 (2014)CrossRefGoogle Scholar
  15. 15.
    Gordan, B., Armaghani, D.J., Hajihassani, M., Monjezi, M.: Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng. Comput. 32(1), 85 (2016)CrossRefGoogle Scholar
  16. 16.
    Gu, Y., Gu, M., Long, Y., Xu, G., Yang, Z., Zhou, J., Qu, W.: An enhanced short text categorization model with deep abundant representation. World Wide Web, 1–15 (2018)Google Scholar
  17. 17.
    Güllü, H., Ercelebi, E.: A neural network approach for attenuation relationships: an application using strong ground motion data from turkey. Eng. Geol. 93(3-4), 65 (2007)CrossRefGoogle Scholar
  18. 18.
    Hasni, H., Alavi, A.H., Jiao, P., Lajnef, N.: Detection of fatigue cracking in steel bridge girders: a support vector machine approach. Archives Civil Mech. Eng. 17(3), 609 (2017)CrossRefGoogle Scholar
  19. 19.
    Huang, Y., Zhang, F., Yashima, A., Ye, W.: Numerical simulation of mitigation for liquefaction-induced soil deformations in a sandy ground improved by cement grouting. Environ. Geol. 55, 1247 (2008)CrossRefGoogle Scholar
  20. 20.
    Ishihara, K.: Soil Behaviors in Earthquake Geotechnics. Oxford Science Publication, Oxford (1996)Google Scholar
  21. 21.
    Jha, S., Suzuki, K.: Reliability analysis of soil liquefaction based on standard penetration test. Comput. Geotech. 36(4), 589 (2009)CrossRefGoogle Scholar
  22. 22.
    Kaya, Z.: Predicting liquefaction-induced lateral spreading by using neural network and neuro-fuzzy techniques. Int. J. Geomechanics 16(4), 04015095 (2016)CrossRefGoogle Scholar
  23. 23.
    Kazama, M., Noda, T.: Damage statistics (summary of the 2011 off the pacific coast of tohoku earthquake damage). Soils Found. 52(5), 780 (2012)CrossRefGoogle Scholar
  24. 24.
    Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Liu, Q.Y.T.Y.: Lightgbm: a highly efficient gradient boosting decision tree. Advan. Neural Inform. Process. Syst., 1–9 (2017)Google Scholar
  25. 25.
    Kohestani, V.R., Hassanlourad, M., Ardakani, A.: Evaluation of liquefaction potential based on cpt data using random forest. Nat. Hazards 79(2), 1079 (2015)CrossRefGoogle Scholar
  26. 26.
    Krawczyk, B., Woniak, M., Schaefer, G.: Cost-sensitive decision tree ensembles for effective imbalanced classification. Appl. Soft Comput. 14, 554 (2014)CrossRefGoogle Scholar
  27. 27.
    Li, H., Wang, Y., Wang, H., Zhou, B.: Multi-window based ensemble learning for classification of imbalanced streaming data. World Wide Web 20, 1507 (2017)CrossRefGoogle Scholar
  28. 28.
    Manek, A.S., Shenoy, P.D., Mohan, M.C., Venugopal, K.R.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and svm classifier. World Wide Web 20, 135 (2017)CrossRefGoogle Scholar
  29. 29.
    Muduli, P.K., Das, S.K.: Model uncertainty of spt-based method for evaluation of seismic soil liquefaction potential using multi-gene genetic programming. Soils Found. 55(2), 258 (2015)CrossRefGoogle Scholar
  30. 30.
    Oka, F., Kimoto, S.: Computational Modelling of Multiphase Geomaterials. CRC Press, Boca Raton (2012)CrossRefGoogle Scholar
  31. 31.
    Poulos, S., Castro, G., France, J.: Liquefaction evaluation procedure. J Geotech. Engrg. 111(6), 772 (1985)CrossRefGoogle Scholar
  32. 32.
    Samui, P., Kim, D., Hariharan, R.: Determination of seismic liquefaction potential of soil based on strain energy concept. Environ. Earth Sci. 74(7), 5581 (2015)CrossRefGoogle Scholar
  33. 33.
    Society, T.J.G.: Soil liquefaction survey in kanto district during the 2011 off the pacific coast of tohoku earthquake. Tech. rep., Ministry of Land, Infrastructure, Transport and Tourism, Kanto Regional Development Bureau. In Japanese (2011)Google Scholar
  34. 34.
    Song, X., Shibasaki, R., Yuan, N.J., Xie, X., Li, T., Adachi, R.: Deepmob: learning deep knowledge of human emergency behavior and mobility from big and heterogeneous data. ACM Transactions on Information Systems (TOIS) 35(4), 41:1 (2017)CrossRefGoogle Scholar
  35. 35.
    Song, X., Zhang, Q., Sekimoto, Y., Horanont, T., Ueyama, S., Shibasaki, R.: Intelligent system for human behavior analysis and reasoning following large-scale disasters. IEEE Intell. Syst. 28(4), 35 (2013)CrossRefGoogle Scholar
  36. 36.
    Song, X., Zhang, Q., Sekimoto, Y., Shibasaki, R., Yuan, N.J., Xie, X.: A Simulator of Human Emergency Mobility following Disasters: Knowledge Transfer from Big Disaster Data. In: AAAI Conference on Artificial Intelligence, pp. 730–736 (2015)Google Scholar
  37. 37.
    Song, X., Zhang, Q., Sekimoto, Y., Shibasaki, R., Yuan, N.J., Xie, X.: Prediction and simulation of human mobility following natural disasters. ACM Transactions on Intelligent Systems and Technology (TIST) 8(2), 29:1 (2017)Google Scholar
  38. 38.
    Sudo, A., Kashiyama, T., Yabe, T., Kanasugi, H., Song, X., Higuchi, T., Nakano, S., Saito, M., Sekimoto, Y.: Particle Filter for Real-time Human Mobility Prediction following Unprecedented Disaster. In: International Conference on Advances in Geographic Information Systems, pp. 5:1–10 (2016)Google Scholar
  39. 39.
    Wang, X.W., Nie, D., Lu, B.L.: Emotional state classification from eeg data using machine learning approach. Neurocomputing 129, 94 (2014)CrossRefGoogle Scholar
  40. 40.
    Wen, Z., Zhang, R., Ramamohanarao, K., Yang, L.: Scalable and fast svm regression using modern hardware. World Wide Web 21, 261 (2018)CrossRefGoogle Scholar
  41. 41.
    Xue, X., Liu, E.: Seismic liquefaction potential assessed by neural networks. Environ. Earth Sci. 76, 1 (2017)CrossRefGoogle Scholar
  42. 42.
    Yang, L., Wang, B., Zhang, R., Zhou, H., Wang, R.: Analysis on location accuracy for the binocular stereo vision system. IEEE Photon. J. 10(1), 7800316:1 (2018)Google Scholar
  43. 43.
    Zhang, J., Lafta, R.L., Tao, X., Li, Y., Chen, F., Luo, Y., Zhu, X.: Coupling a fast fourier transformation with a machine learning ensemble model to support recommendations for heart disease patients in a telehealth environment. IEEE Access 5, 10674 (2017)CrossRefGoogle Scholar
  44. 44.
    Zhang, X., Hu, B., Chen, J., Moore, P.: Ontology-based context modeling for emotion recognition in an intelligent Web. World Wide Web 16, 497 (2013)CrossRefGoogle Scholar
  45. 45.
    Zhao, X., Bi, X., Qiao, B.: Probability based voting extreme learning machine for multiclass xml documents classification. World Wide Web 17, 1217 (2014)CrossRefGoogle Scholar
  46. 46.
    Zheng, W., Tang, H., Qian, Y.: Collaborative work with linear classifier and extreme learning machine for fast text categorization. World Wide Web 18, 235 (2015)CrossRefGoogle Scholar

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

Personalised recommendations