基于主成分分析的GA-SVM地表沉降预测模型

Application of GA-SVM model based on principal component nalysis to prediction of surface settlement of shield construction

本文提出了基于主成分分析(principal component analysis,PCA)的遗传算法(genetic algorithm,GA)优化支持向量机(support vector machine,SVM)地表沉降预测模型,并结合广州地铁二号线35组地表沉降的实测数据,对比该模型与GA-SVM模型和Levenberg-Marquardt算法改进的BP神经网络模型(LM-BP)的预测效果。结果表明,基于主成分分析的GA-SVM预测模型较其他2种模型的计算速度更快,泛化能力和拟合效果更好,预测值更精确,对预测盾构施工引起的地表沉降量具有一定的使用价值。

This paper proposes a principal component analysis（PCA）genetic algorithm（GA）to optimize the support vector machine（SVM）surface subsidence prediction model,and combines the surface subsidence of 35 groups of Guangzhou Metro Line 2.The measured data compares the prediction effect of the model with the GA-SVM model and the improved BP neural network model（LM-BP）of the Levenberg-Marquardt algorithm.The results show that the GA-SVM prediction model based on principal component analysis is faster than other two models,the generalization ability and fitting effect are better,the prediction value is more accurate,and the surface settlement caused by predicting shield construction has Certain use value.