大华滑坡位移预测模型研究

针对传统滑坡位移预测过程中的不足,提出了一种基于最小二乘支持向量机(LSSVM)的滑坡位移预测方法。以某流域大华滑坡为例,基于时序分析和集合经验模态分解法(EEMD)将原始序列重构为趋势项和波动项,趋势项位移受滑坡内部因素影响,采用最小二乘法与多项式方程进行拟合预测;波动项位移受库水位、降雨、地下水位等周期性因素影响,结合灰色关联度法和核主成分分析法(KPCA)对输入因子进行筛选与降维,并用粒子群算法-最小二乘支持向量机耦合模型(PSO-LSSVM)进行建模预测。最后将趋势项与周期项预测位移相加得到累计预测位移,并对模型预测精度进行定量分析。结果表明,建立的EEMD-KPCA-PSO-LSSVM组合模型预测效果良好,较传统BP神经网络、LSSVM等单一模型有着更高的预测精度,可为同类型滑坡位移预测提供新的思路。

A Displacement Prediction Model for Dahua Landslide

A method of landslide displacement prediction based on least squares support vector machine (LSSVM) is proposed to overcome the shortcomings of traditional prediction for Dahua landslide in Lancang River basin as a case study. The original sequence is decomposed and reconstructed into trend item and fluctuation item based on time series analysis and ensembled empirical mode decomposition (EEMD). The displacement of trend item is affected by internal factors of landslide,and the least square method and polynomial equation are used for fitting prediction. The displacement of fluctuation item is affected by periodic factors such as reservoir water level,rainfall and groundwater level. The grey correlation degree method and kernel principal component analysis (KPCA) are combined to screen and reduce the dimension of input factors,and then PSO-LSSVM coupling model is used for predicting the fluctuation item. Finally,the cumulative predicted displacement is obtained by adding the predicted displacement of trend item and fluctuation item,and the model prediction accuracy is quantitatively analyzed. Results demonstrate that the EEMD-KPCA-PSO-LSSVM combined model has a sound prediction performance with higher prediction accuracy than traditional BP neural network,LSSVM and other single models.