河北地质大学学报2023，Vol.46Issue(6)：41-46,6.DOI:10.13937/j.cnki.hbdzdxxb.2023.06.006
基于遗传算法优化支持向量机的震级预测模型研究
Earthquake Magnitude Prediction Model Based on Support Vector Machine Optimized by Genetic Algorithm
摘要
Abstract
Based on the complex nonlinear relationship between magnitude and its impact indicators,support vector machine(SVM)was introduced to predict earthquake magnitude.Principal component analysis(PCA)was used to reduce the dimensions of the impact indicator set.At the same time,the newly generated principal component was used as the model input,and genetic algorithm(GA)was used to find the optimal parameters of SVM,the earthquake magnitude prediction model based on PCA-GA-SVM was established.The reliability of the model performance was verified by actual test samples.The results show that the average relative error of the prediction results of PCA-GA-SVM model is 2.13%,which has good prediction effect.关键词
地震震级/主成分分析法/遗传算法/支持向量机Key words
earthquake magnitude/principal component analysis/genetic algorithm/support vector machine分类
建筑与水利引用本文复制引用
张小涛,张新东,王晨晖..基于遗传算法优化支持向量机的震级预测模型研究[J].河北地质大学学报,2023,46(6):41-46,6.基金项目
唐山震源区密集台阵观测与孕震环境研究(DZ20200827056) （DZ20200827056）
河北省地震科技星火计划(DZ2021110500001). （DZ2021110500001）
