基于RS-PCA-GA-SVM的砂土液化预测方法研究

砂土液化是一种危害性比较大的自然灾害,对砂土液化进行判定预测在地质灾害防治领域中有重要的研究意义。通过粗糙集理论(Rough Set,RS)对影响砂土液化的6个初始评价指标(包括震级、土深、震中距、地下水位、标贯击数和地震持续时间)进行属性约简,去掉冗余或干扰信息,得到基于4个核心预测指标的数据集。通过主成分分析法(Principal Component Analysis,PCA)从核心评价指标中提取出主成分,采用支持向量机(Support Vector Machine,SVM)对数据集进行训练,用遗传算法(Genetic Algorithm,GA)优化参数,建立砂土液化的RS-PCA-GA-SVM预测模型。并结合砂土液化实际数据将预测结果与基于Levenberg-Marquardt算法改进的BP神经网络模型(LM-BP)的预测结果做比较。实例计算表明:基于RS-PCA-GA-SVM模型得到的砂土液化预测结果精度较LM-BP神经网络有很大的提高,判别结果与实际情况比较吻合,可在实际工程中应用。

A Method of Predicting Sand Liquefaction Based on RS-PCA-GA-SVM

Sand liquefaction is a harmful natural disaster, and it is of great importance to evaluate and predict sand liquefaction in the field of geological disaster prevention and control. In this paper, the rough set theory (RS) was used to perform attribute reduction on six initial evaluation indices, including magnitude, depth of soil, epicentral distance, groundwater level, standard penetration test blow count, and earthquake duration, all of which affect sand liquefaction. After removing redundant or interference information, we obtained a data set based on four core predictors. The principal component analysis (PCA) method was then used to extract the principal component from the four-core evaluation indices. The support vector machine (SVM) was used to train the data set, and the genetic algorithm (GA) was used to optimize the parameters. Finally, the RS-PCA-GA-SVM prediction model for sand liquefaction was established. Combined with the actual data of sand liquefaction, the predicted result of the proposed model was compared with that of the back propagation (BP) neural network model based on the improved Levenberg-Marquardt algorithm (LM-BP). The calculated results showed that the accuracy of sand liquefaction prediction results based on a RS-PCA-GA-SVM model are much better than those of the LM-BP neural network. The discriminant results were in good agreement with the actual situation and can be applied in practical engineering.