| 基于PCA-PSO-SVM的地震死亡人数预测模型研究 |
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| 引用本文： | 刘立申,王晨晖,王利兵,陈凯男,吴鹤帅.基于PCA-PSO-SVM的地震死亡人数预测模型研究[J].地震地磁观测与研究,2019,40(5):41-47. |
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| 作者姓名： | 刘立申 王晨晖 王利兵 陈凯男 吴鹤帅 |
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| 作者单位： | 中国河北 054000 红山基准地震台 |
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| 基金项目： | 河北省地震科技星火计划（项目编号：DZ20160405023） |
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| 摘 要： | 为准确预测地震死亡人数,提出了基于主成分分析法(PCA)和粒子群算法(PSO)优化的支持向量机(SVM)模型。首先利用主成分分析法对地震死亡人数7个影响因子中的6个进行数据降维,同时对第7个发震时刻因子单独进行区间分类,然后对提取出的主成分进行归一化处理,将归一化的主成分数据作为支持向量机的输入向量,通过粒子群算法寻优获得最优支持向量机模型参数,最终建立基于PCA-PSO-SVM的地震死亡人数预测模型,并对5组样本进行死亡人数预测,同时对比分析包含和不包含发震时刻因子的2种情况下的模型预测效果。结果表明:在不考虑发震时刻因子的情况下,使用PCA-PSO-SVM模型的最小误差、最大误差和平均误差分别为0.85%、20%、10%,其平均误差相比PSO-SVM、SVM模型分别降低2.08%、2.28%;输入向量加入发震时刻因子分类数据后,PCA-PSO-SVM模型的最小误差、最大误差和平均误差分别为0.25%、20%、7.18%,其平均误差相比PSO-SVM、SVM模型分别降低3.34%、3.50%。因此,加入发震时刻因子后3种模型的平均误差明显降低,同时由于PCA-PSO-SVM模型进行主成分降维处理,能够明显提高运行效率和预测精度,故降低了模型复杂度。
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| 关 键 词： | 地震死亡人数 主成分分析法 粒子群算法 支持向量机 |
Earthquake casualties prediction model based on PCA-PSO-SVM |
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| Liu Lishen,Wang Chenhui,Wang Libing,Chen Kainan,Wu Heshuai.Earthquake casualties prediction model based on PCA-PSO-SVM[J].Seismological and Geomagnetic Observation and Research,2019,40(5):41-47. |
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| Authors: | Liu Lishen Wang Chenhui Wang Libing Chen Kainan Wu Heshuai |
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| Institution: | Hongshan Benchmark Seismic Station, Hebei Province 054000, China |
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| Abstract: | In order to predict earthquake casualties accurately, support vector machine (SVM) model optimized by genetic algorithm (PSO) based on principle component analysis (PCA) was proposed. Making the data dimension reduction to 6 factors from 7 impact factors of earthquake casualties using PCA, classifying the origin time of earthquake by intervals, normalizing the extracted principal components which were used as input vectors of support vector machine and optimizing the best SVM parameters using PSO, finally the prediction model for earthquake casualties based on PCA-PSO-SVM was established which was used to predict the casualties of 5 samples. The prediction model results considering the earthquake origin time factors or not were compared. The result shows the minimum error, maximum error and average error of PCA-PSO-SVM model were 0.85%, 20% and 10% respectively without considering the earthquake origin time factor. Compared with PSO-SVM model and SVM model, the average error of PCA-PSO-SVM model is reduced by 2.08% and 2.28% respectively. After the classified data of origin time factor was added in input vectors, the minimum error, maximum error and average error of PCA-PSO-SVM model were 0.25%, 20% and 7.18% respectively. Compared with PSO-SVM model and SVM model, the average error of PCA-PSO-SVM model is reduced by 3.34% and 3.50%, respectively. Therefore, the average error of three models was reduced obviously after adding the earthquake origin time factor, and PCA-PSO-SVM model can improve the operation efficiency and prediction accuracy obviously and reduced the complexity of the model duo to dimension reduction. |
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| Keywords: | earthquake casualties principal component analysis particle swarm algorithm support vector machine |
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