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Natural Hazards

, Volume 94, Issue 3, pp 999–1021 | Cite as

An earthquake casualty prediction model based on modified partial Gaussian curve

  • Xing Huang
  • Huidong Jin
Original Paper
  • 47 Downloads

Abstract

Earthquake casualty prediction is crucial for efficient and effective emergency management and response. In order to improve prediction reliability of earthquake casualties, correlation analysis and principal component analysis are used to select prediction covariates. Finally, five key indexes, including magnitude, epicenter intensity, population density, earthquake occurrence time and damaged building area, are chosen. According to the “two-stage” rule of earthquake casualties, a prediction model based on the modified partial Gaussian curve is proposed. In order to improve its prediction accuracy, the paper looked epicenter intensity and the casualty as the variables. And the partial Gaussian curve prediction model is modified by using the magnitude coefficient, population density coefficient, earthquake occurrence time coefficient and damaged building coefficient. The cross-validation experimental results show that the modified partial Gaussian curve has the advantages of good stability and high prediction accuracy comparing with the high-order nonlinearity, logarithmic curve, multivariate linearity, artificial neural network and so on. It can be used in practice from earthquake casualty prediction.

Keywords

Earthquake disaster casualties Prediction model Partial Gaussian curve 

Notes

Acknowledgements

The paper is supported by the Western Project of the National Social Science Fund (No. 18XGL016). We thank Huidong Jin, the famous scientist of CSIRO, data 61, Australia, for his help, experts and journal editors who reviewed this article and all scholars who provided references.

References

  1. Aghamohammadi H, Mesgari MS, Mansourian A, Molaei D (2013) Seismic human loss estimation for an earthquake disaster using neural network. Int J Environ Sci Technol 10(5):931–939CrossRefGoogle Scholar
  2. Aiko F, Robin S, Yutaka O et al (2010) Analytical study on vulnerability functions for casualty estimation in the collapse of adobe building induced by earthquake. Bull Earthq Eng 8:451–479CrossRefGoogle Scholar
  3. Ara S (2013) Analyzing population distribution and its effect on earthquake loss estimation in Sylhet, Bangladesh. Dissertation, University of TwenteGoogle Scholar
  4. He M, Zhou W (2011) Prediction of seismic casualties based on the seismic damage index. J Harbin Inst Technol 43(4):23–27Google Scholar
  5. Jie Z, Huizhen G, Qi L (2011) Study on the model of earthquake casualty estimation based on wenchuan earthquake. China Saf Sci J 21(3):59–64Google Scholar
  6. Liu J, Lin J (2012) Study on the evaluation method of earthquake casualties based on epicentral intensity. J Nat Disasters 21(5):113–119Google Scholar
  7. Ma Y, Xie L (2000) Methodologies for assessment of earthquake casualty. Earthq Eng Eng Dyn 24(4):140–147Google Scholar
  8. Max W, Sushil G, Philippe R (2017) Casualty estimates in two up-dip complementary Himalayan earthquakes. Seismol Res Lett 88(6):1508–1515CrossRefGoogle Scholar
  9. Muhammet G, Ali FG (2016) An artificial neural network based on earthquake casualty estimation model for Istanbul city. Nat Hazards 84:2163–2178CrossRefGoogle Scholar
  10. Samardjieva E, Oike K (1992) Modeling the number of casualties from earthquake. J Nat Disaster Sci 14(1):17–28Google Scholar
  11. Shan Y, Haixia W, Yajie M (2005) Three-layer BP network model for estimation of casualties in an earthquake. J Earthq Eng Eng Vib 25(6):113–117Google Scholar
  12. Shapira S, Aharonson-Daniel L et al (2015) Integrating epidemiological and engineering approaches in the assessment of human casualties in earthquakes. Nat Hazards 78(7):1447–1462CrossRefGoogle Scholar
  13. Shi W, Chen K, Xie Y et al (2012) Study on the estimation method of casualties of earthquake victims in Yunnan. J Seismol Res 35(3):387–392Google Scholar
  14. Tian X, Zhu W (2012) Study on earthquake casualty estimation model based on principal component analysis and BP neural network analysis. Northwest Seismol J 34(4):365–368Google Scholar
  15. Wang X et al (2011) ANN model for the estimation of life casualties in earthquake engineering. Syst Eng Procedia 1:55–60CrossRefGoogle Scholar
  16. Wen BC, Jiang C (2013) Forecasting emergency demand based on BP neural network and principal component analysis. Adv Inf Serv Sci 5(13):38–45Google Scholar
  17. Wu H, Feng T, Hong Z (2013) Earthquake casualties prediction model based on remote sensing images. J Tongji Univ (Med Sci) 34(5):36–39Google Scholar
  18. Xin T, Zhu R (2012) Study on earthquake casualties forecasting model based on principal component analysis and BP neural network analysis. China Earthq Eng J 34(4):365–368Google Scholar
  19. Xing H, Zhonglin Z, Shaoyu W (2015) The prediction model of earthquake casualty based on robust wavelet v-SVM. Nat Hazards 77:717–732CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.School of Economic and ManagementSouthwest University of Science and TechnologyMianyang CityChina
  2. 2.CSIRO Data61CanberraAustralia

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