基于PCA-PSO-ELM模型预测地震死亡人数研究  

Research on Prediction of Earthquake Death Toll Based on PCA-PSO-ELM Model

作　　者：陈韶金 刘子维 周浩[2,3] 江颖 翟笃林 CHEN Shaojin;LIU Ziwei;ZHOU Hao;JIANG Ying;ZHAI Dulin(School of Information Engineering,Institute of Disaster Prevention,465 Xueyuan Street,Sanhe 065201,China;Institute of Seismology,CEA,40 Hongshance Road,Wuhan 430071,China;Wuhan Gravitation and Solid Earth Tides,National Observation and Research Station,40 Hongshance Road,Wuhan 430071,China)

机构地区：[1]防灾科技学院信息工程学院,河北省三河市065201 [2]中国地震局地震研究所,武汉市430071 [3]武汉引力与固体潮国家野外观测研究站,武汉市430071

出　　处：《大地测量与地球动力学》2024年第1期105-110,共6页Journal of Geodesy and Geodynamics

基　　金：中国地震局地震研究所和应急管理部国家自然灾害防治研究院基本科研业务费(IS202226326,IS202236336);中国地震局地震应急青年重点任务(CEA_EDEM(ITNS)-2023)。

摘　　要：筛选42个历史地震震例,对地震震级、震源深度、震中烈度、抗震设防烈度、震中烈度与抗震设防烈度之差(ΔL)、人口密度以及发震时刻7个影响指标进行主成分分析(principal components analysis,PCA),构建粒子群优化(particle swarm optimization,PSO)极限学习机(extreme learning machine,ELM)地震死亡人数预测模型。将37个震例数据进行预处理和训练,并使用5个震例数据来检验模型的预测精度。实验结果表明,该PCA-PSO-ELM组合模型的平均误差率为10.87%,相比于PCA-ELM模型和ELM模型,其平均误差率分别降低8.70个百分点和18.38个百分点。因此,采用PCA-PSO-ELM组合模型预测地震死亡人数具有一定的可行性。We screen 42 historical earthquake cases,and conduct principal components analysis(PCA)on seven impact factors,such as earthquake magnitude,source depth,epicenter intensity,seismic intensity,difference between epicenter intensity and seismic intensity(ΔL),population density,and occurrence moment,and construct an earthquake death toll prediction model based on particle swarm optimization(PSO)extreme learning machine(ELM).We pre-process and train the data of 37 earthquake cases,and test the accuracy of the model using the data of 5 earthquake cases.The experimental results show that the average error rate of the proposed combined PCA-PSO-ELM model is 10.87%,which is 8.70 percent points and 18.38 percent points lower than that of the PCA-ELM model and ELM model,respectively.Therefore,the combined PCA-PSO-ELM model is feasible for earthquake death toll prediction.

关 键 词：地震死亡人数预测 主成分分析 粒子群优化 极限学习机 震后评估 

分 类 号：P315[天文地球—地震学]