基于PCA-GSM-SVM的地震伤亡人数预测

针对影响地震伤亡人数的评价指标数量较多且各指标之间存在着复杂的非线性关系,运用机器学习理论,提出了基于支持向量机(Support Vector Machine)的地震伤亡人数预测模型;首先利用主成分分析法(Principle Component Analysis)对7个地震死亡人数影响指标进行数据降维,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预测模型的输入向量,将地震伤亡人数作为预测模型的输出向量;以27个地震伤亡实例作为学习样本进行训练,运用网格搜索法(Grid Search Method)寻优获得最优支持向量机参数,最终建立基于PCA-GSM-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。结果表明:PCA-GSM-SVM模型的最小误差、最大误差和平均误差分别为5.12%、15.7%和9.16%,其平均误差相比于GSM-SVM模型和SVM模型分别降低6.51%和7.11%,因此PCA-GSM-SVM模型预测精度较高,可在工程实际中推广。

Earthquake Casualties Prediction Based on PCA-GSM-SVM

As earthquake casualties involved a complex of non-linear relationship between a variety of evaluation indicators,a model of support vector machine(SVM)to predict earthquake casualties based on machine learning theory was proposed.First,using principle component analysis(PCA)to make data dimension reduction for 7 influencing factors of earthquake casualties,then normalizing the extracted principal components which were used as input vectors of support vector machine,and earthquake casualties was used as output vectors,27 typical cases of earthquake casualties were used for training data,then using grid search method(GSM)to optimize the best SVM parameters,finally the prediction model for earthquake casualties based on PCA-GSM-SVM was established,and it was used to predict the casualties of 5 samples.The result shows that the minimum error,maximum error and average error of PCA-GSM-SVM model were 5.12%,15.7% and 9.16% respectively.The average error of PCA-GSM-SVM model is reduced by 6.51%and 7.11%,respectively.Therefore,PCA-GSM-SVM model has high prediction accuracy and can be popularized in engineering practice.