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The casualty prediction of earthquake disaster based on Extreme Learning Machine method

Author

Listed:
  • Huang Xing

    (Southwest University of Science and Technology)

  • Song Junyi

    (Southwest University of Science and Technology)

  • Huidong Jin

    (Commonwealth Science and Industry Research Organization)

Abstract

In the prediction of casualties of earthquake disaster, the traditional prediction method requires strict sample data, and it is necessary to manually set a large number of parameters, resulting in poor prediction accuracy and slow learning speed. This paper introduces the Extreme Learning Machine (ELM) into the earthquake casualty prediction, aiming to improve the prediction accuracy. Through the data training, the ELM network structure of earthquake victims’ casualty prediction is established, and the number of hidden layer nodes and the excitation function are determined, which ensures the reliability of the ELM network prediction results. Based on the data of 84 groups of earthquake victims from China in 1970–2017, the ELM algorithm, BP neural network, SVM and modified partial Gaussian curve were compared and verified. The results show that the average relative error of ELM algorithm for earthquake disaster prediction is 3.37%, the coefficient of determination R-square is 0.96, the average relative error of injury prediction is 1.04%, and the coefficient of determination R-square is 0.97, which indicates that the ELM algorithm has good robustness and generalization ability.

Suggested Citation

  • Huang Xing & Song Junyi & Huidong Jin, 2020. "The casualty prediction of earthquake disaster based on Extreme Learning Machine method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(3), pages 873-886, July.
  • Handle: RePEc:spr:nathaz:v:102:y:2020:i:3:d:10.1007_s11069-020-03937-6
    DOI: 10.1007/s11069-020-03937-6
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    References listed on IDEAS

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    1. Huang Xing & Zhou Zhonglin & Wang Shaoyu, 2015. "The prediction model of earthquake casuailty based on robust wavelet v-SVM," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(2), pages 717-732, June.
    2. Xing Huang & Huidong Jin, 2018. "An earthquake casualty prediction model based on modified partial Gaussian curve," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(3), pages 999-1021, December.
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    Citations

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    Cited by:

    1. Chen, Weiyi & Zhang, Limao, 2022. "An automated machine learning approach for earthquake casualty rate and economic loss prediction," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. Jiaqi Zhang & Xijun He, 2023. "Earthquake magnitude prediction using a VMD-BP neural network model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 189-205, May.

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