Application of improved ELM algorithm in the prediction of earthquake casualties

Xing Huang, Mengjie Luo and Huidong Jin

PLOS ONE, 2020, vol. 15, issue 6, 1-13

Abstract: Background: Earthquake casualties prediction is a basic work of the emergency response. Traditional forecasting methods have strict requirements on sample data and lots of parameters are required to be set manually, which can result in poor results with low prediction accuracy and slow learning speed. Method: In this paper, the Extreme Leaning Machine (ELM) is introduced into the earthquake disaster casualty predictions with the purpose of improving the prediction accuracy. However, traditional ELM model still has the problems of poor network structure stability and low prediction accuracy. So an Adaptive Chaos Particle Swarm Optimization (ACPSO) is proposed to the optimize traditional ELM’s network parameters to enhance network stability and prediction accuracy, and the improved ELM model is applied to earthquake disaster casualty prediction. Results: The experimental results show that the earthquake disaster casualty prediction model based on ACPSO-ELM algorithm has better stability and prediction accuracy. Conclusion: ACPSO-ELM algorithm has better practicality and generalization in earthquake disaster casualty prediction.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0235236

DOI: 10.1371/journal.pone.0235236

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