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An Earthquake Fatalities Assessment Method Based on Feature Importance with Deep Learning and Random Forest Models

Author

Listed:
  • Hanxi Jia

    (Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China)

  • Junqi Lin

    (Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China)

  • Jinlong Liu

    (Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China)

Abstract

This study aims to analyze and compare the importance of feature affecting earthquake fatalities in China mainland and establish a deep learning model to assess the potential fatalities based on the selected factors. The random forest (RF) model, classification and regression tree (CART) model, and AdaBoost model were used to assess the importance of nine features and the analysis showed that the RF model was better than the other models. Furthermore, we compared the contributions of 43 different structure types to casualties based on the RF model. Finally, we proposed a model for estimating earthquake fatalities based on the seismic data from 1992 to 2017 in China mainland. These results indicate that the deep learning model produced in this study has good performance for predicting seismic fatalities. The method could be helpful to reduce casualties during emergencies and future building construction.

Suggested Citation

  • Hanxi Jia & Junqi Lin & Jinlong Liu, 2019. "An Earthquake Fatalities Assessment Method Based on Feature Importance with Deep Learning and Random Forest Models," Sustainability, MDPI, vol. 11(10), pages 1-21, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2727-:d:230816
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    References listed on IDEAS

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    1. Max Wyss, 2005. "Human Losses Expected in Himalayan Earthquakes," 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. 34(3), pages 305-314, March.
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    Cited by:

    1. Manhao Luo & Shuangyun Peng & Yanbo Cao & Jing Liu & Bangmei Huang, 2023. "Earthquake fatality prediction based on hybrid feature importance assessment: a case study in Yunnan Province, China," 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. 116(3), pages 3353-3376, April.
    2. Hariklia D. Skilodimou & George D. Bathrellos, 2021. "Natural and Technological Hazards in Urban Areas: Assessment, Planning and Solutions," Sustainability, MDPI, vol. 13(15), pages 1-5, July.

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