Natural Hazards

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

An earthquake casualty prediction model based on modified partial Gaussian curve

  • Xing HuangEmail author
  • Huidong Jin
Original Paper


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.


Earthquake disaster casualties Prediction model Partial Gaussian curve 



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.


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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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