Abstract: Many landslides in Three Gorges Reservoir area are featured by stepwise increasing surface displacement along the time. Responding composition model is one of the main methods for displacement prediction of landslides. Currently, high-frequency and low-frequency components of inducing factors are usually ignored. A method based on reconstruction of time series by ensemble empirical mode decomposition (EEMD) and particle swarm optimization based support vector machine regression (PSO-SVR) prediction for displacement is proposed. The typical Baishuihe landslide in Three Gorges Reservoir is taken as an example. Firstly, the monitored surface displacement time series from July 2003 to March 2013 is decomposed into trend and fluctuant components by EEMD. The trend component can be predicted using quadratic polynomial equation fitted by the least square method. With EEMD and t-test methods, rainfalls and reservoir levels time series are reconstructed into high-frequency rainfalls, low-frequency rainfalls, high-frequency reservoir levels and low-frequency reservoir levels, respectively. Combined with other common factors, high-frequency rainfalls and monthly variations of reservoir levels are selected as predominant factors for fluctuant displacement components by method of gray relational analysis (GRA). Finally, PSO-SVR is utilized for prediction purpose. Application results show that average relative error is 0.009 8, variance ratio is 0.023 9 and small error probability is 1, which demonstrate preferable effect of the proposed method. For verification and testing, 5 other typical landslides in Three Gorges Reservoir area are presented to test the effectiveness of our method, which can provide references for similar landslides.

Key words: Three Gorges Reservoir, landslide displacement prediction, inducing factors, ensemble EMD, PSO-SVR