Abstract
Reliable earthquake fatality prediction is an important reference for post-earthquake emergency response efforts. Seismic data are the basis for constructing earthquake casualty prediction models, but the selection and evaluation of earthquake features are more critical due to the scarcity of destructive earthquake samples. In order to make full use of the high-dimensional survey data of destructive earthquake disasters in the Earthquake Reports in Yunnan Province since 1992, and effectively use it to improve the ability to predict the number of earthquake casualties, this paper proposes a hybrid feature importance evaluation method based on four conventional feature contribution methods (IG, PPMCC, SRCC and MDI), ranking the importance of 63 features that affect the number of earthquake casualties in Yunnan Province, and reducing the feature dimension accordingly. Then, cross-validation is used to compare the accuracy of the four machine models before and after dimensionality reduction. We found that (1) among the 10 features with the highest hybrid importance, there were 8 population distribution features, 1 geological hazard feature (number of landslides) and 1 damage degree feature (highest intensity of earthquakes); (2) the feature dimensionality reduction based on the importance of hybrid features can effectively improve the prediction accuracy of machine learning models; and (3) in the comparison of several methods, the Particle Swarm Optimized Support Vector Machine model had the highest prediction accuracy, with an R2 over 0.934. The research results showed that this method can significantly improve the prediction accuracy of the machine learning model and has some reference value for earthquake emergency rescue and post-disaster reconstruction work.
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Funding
This research was funded by the National Natural Science Foundation of China (Grant Nos. 41971369, 41561086, 42171392, 41861048); Yunnan Province Science and Technology Fundamental Special Key Project-Research and Application of Key Technologies for Hybrid Enhanced Smart Space Crowdsourcing in Smart Border Control between China and Myanmar (202001AS070032)(2020–2023); Yunnan Provincial High-level Science and Technology Talents and Innovation Team Selection Special Project—Reserve Talents for Young and Middle-aged Academic and Technical Leaders (202205AC160014).
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YC and SP contributed to conceptualization; ML and JL contributed to methodology; ML and JL contributed to software; ML and YC contributed to validation; ML, SP and YC contributed to formal analysis; YC contributed to investigation; YC contributed to resources; ML and JL contributed to data curation; ML contributed to writing—original draft preparation; SP and YC contributed to writing—review and editing; ML contributed to visualization; SP and BH contributed to supervision; SP and BH contributed to funding acquisition; all authors have read and agreed to the published version of the manuscript.
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Luo, M., Peng, S., Cao, Y. et al. Earthquake fatality prediction based on hybrid feature importance assessment: a case study in Yunnan Province, China. Nat Hazards 116, 3353–3376 (2023). https://doi.org/10.1007/s11069-023-05812-6
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DOI: https://doi.org/10.1007/s11069-023-05812-6