Landslide displacement prediction based on CEEMDAN and grey wolf optimized-support vector regression model

Abstract

Landslide prediction is very important and challenging for reducing geological hazards. In the Three Gorges Reservoir area, landslides show stepped deformation due to seasonal rainfall and periodic fluctuation of reservoir water level. The purpose of this study is to use complete ensemble empirical mode decomposition with adaptive noise and grey wolf optimization to support the vector regression method for displacement prediction. Firstly, the cumulative displacement is decomposed by CEEMDAN to obtain both trend term and fluctuation term displacement. Secondly, according to the cumulative displacement, rainfall, and reservoir water level data, the influencing factors related to the displacement of the trend term and the fluctuation term are determined. Then, the GWO-SVR model is used to predict the trend and fluctuation displacement. The final prediction result is obtained by adding the calculated predicted displacement values of each component. The Shuizhuyuan landslide in the Three Gorges Reservoir area, China, was taken as an example, and the long-term displacement data of monitoring point SZY-03 were selected for analysis. The results show that the root mean square error (RMSE) and coefficient of determination (R2) between the measured displacement values and the prediction values were 0.9845 and 0.9964, respectively. The trained model has high computational accuracy, which proves that the GWO-SVR model can be used for displacement prediction of this type of landslide in the Three Gorges Reservoir area.