世界地震工程2024,Vol.40Issue(1) :199-205.DOI:10.19994/j.cnki.WEE.2024.0019

基于BAS-ELM的地震经济损失预测

Prediction of earthquake economic losses based on BAS-ELM

王晨晖 袁颖 吕国军
世界地震工程2024,Vol.40Issue(1) :199-205.DOI:10.19994/j.cnki.WEE.2024.0019

基于BAS-ELM的地震经济损失预测

Prediction of earthquake economic losses based on BAS-ELM

王晨晖 1袁颖 2吕国军3
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作者信息

  • 1. 河北红山巨厚沉积与地震灾害国家野外科学观测研究站,河北 邢台 054000;河北省地震局邢台地震监测中心站,河北 邢台 054000
  • 2. 河北地质大学 城市地质与工程学院,河北 石家庄 050031;河北省地下人工环境智慧开发与管控技术创新中心,河北 石家庄 050031
  • 3. 河北红山巨厚沉积与地震灾害国家野外科学观测研究站,河北 邢台 054000;河北省地震局,河北 石家庄 050031
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摘要

为提高地震经济损失预测的准确性和有效性,提出了基于天牛须算法(beetle antennae search,BAS)优化极限学习机(extreme learning machine,ELM)的地震经济损失预测模型.以 1996-2014 年破坏性地震经济损失为样本数据,选取震级、震中烈度、人口密度和人均GDP等 4 个影响指标作为模型的输入向量,直接经济损失为输出向量,同时利用BAS优化ELM模型变量,从而消除了随机变量对预测结果的影响,最终建立基于BAS-ELM的地震经济损失预测模型.将建好的BAS-ELM模型用于测试样本的预测,并同其它模型进行了比较.结果表明:BAS-ELM的预测准确率为97.244%,具有更好的预测精度.

Abstract

In order to improve the accuracy and effectiveness of earthquake economic losses prediction,a prediction model for earthquake economic losses based on ELM optimized by BAS was proposed.Using the economic losses of destructive earthquakes from 1996 to 2014 as sample data,four influencing factors such as magnitude,epicenter intensity,population density,and per capita GDP were selected as input vectors,and direct economic losses were used as output vectors.BAS was used to optimize the model variables,thereby the influence of random variables was eliminated on the prediction results.Finally,earthquake economic loss prediction model based on BAS-ELM was established.Then BAS-ELM model was used to predict the test samples and compared with other models.The result shows that the prediction accuracy of BAS-ELM is 97.244%,which has better prediction accuracy.

关键词

地震经济损失/极限学习机/天牛须算法

Key words

earthquake economic losses/extreme learning machine/beetle antennae search algorithm

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基金项目

国家自然科学基金(41807231)

河北地质大学科技创新团队项目(KJCXTD-2021-08)

河北省地震科技星火计划重点项目(DZ2021121600001)

河北省重点研发计划项目(22375406D)

出版年

2024
世界地震工程
中国地震局工程力学研究所 中国力学学会

世界地震工程

CSTPCD北大核心
影响因子:0.523
ISSN:1007-6069
参考文献量13
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