School of Economics and Management, Southwest University of Science and Technology, Mianyang, China.
CSIRO Data61, Canberra, Australia.
PLoS One. 2020 Jun 25;15(6):e0235236. doi: 10.1371/journal.pone.0235236. eCollection 2020.
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.
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.
The experimental results show that the earthquake disaster casualty prediction model based on ACPSO-ELM algorithm has better stability and prediction accuracy.
ACPSO-ELM algorithm has better practicality and generalization in earthquake disaster casualty prediction.
地震伤亡预测是应急响应的一项基础工作。传统的预测方法对样本数据有严格的要求，并且需要手动设置大量参数，这可能导致预测精度低、学习速度慢的结果。
本文将极限学习机（ELM）引入地震灾害伤亡预测中，旨在提高预测精度。然而，传统的 ELM 模型仍然存在网络结构稳定性差和预测精度低的问题。因此，提出了一种自适应混沌粒子群优化（ACPSO）算法来优化传统 ELM 的网络参数，以增强网络稳定性和预测精度，并将改进的 ELM 模型应用于地震灾害伤亡预测。
实验结果表明，基于 ACPSO-ELM 算法的地震灾害伤亡预测模型具有更好的稳定性和预测精度。
ACPSO-ELM 算法在地震灾害伤亡预测中具有更好的实用性和泛化性。
