Displacement characteristics and prediction of Baishuihe landslide in the Three Gorges Reservoir Displacement characteristics and prediction of Baishuihe landslide in the Three Gorges Reservoir

最小化 最大化

Vol16 No.9: 2203-2214

Title】Displacement characteristics and prediction of Baishuihe landslide in the Three Gorges Reservoir

Author】LI De-ying1*; SUN Yi-qing1; YIN Kun-long1; MIAO Fa-sheng1; Thomas GLADE2; Chin LEO3

Addresses】1 Faculty of Engineering, China University of Geosciences, Wuhan 430074, China; 2 Department of Geography and Regional Research, University of Vienna, Vienna 1010, Austria; 3 School of Computing, Engineering and Math, Western Sydney University, Sydney, NSW 1797, Australia

Corresponding author】LI De-ying

Citation】Li DY, Sun YQ, Yin KL, et al. (2019) Displacement characteristics and prediction of Baishuihe landslide in the Three Gorges Reservoir. Journal of Mountain Science 16(9). https://doi.org/10.1007/s11629-019-5470-3

DOI】https://doi.org/10.1007/s11629-019-5470-3

Abstract】In order to reach the designated final water level of 175 m, there were three impoundment stages in the Three Gorges Reservoir, with water levels of 135 m, 156 m and 175 m. Baishuihe landslide in the Reservoir was chosen to analyze its displacement characteristics and displacement variability at the different stages. Based on monitoring data, the landslide displacement was mainly influenced by rainfall and drawdown of the reservoir water level. However, the magnitude of the rise and drawdown of the water level after the reservoir water level reached 175 m did not accelerate landslide displacement. The prediction of landslide displacement for active landslides is very important for landslide risk management. The time series of cumulative displacement was divided into a trend term and a periodic term using the Hodrick-Prescott (HP) filter method. The polynomial model was used to predict the trend term. The extreme learning machine (ELM) and least squares support vector machine (LS-SVM) were chosen to predict the periodic term. In the prediction model for the periodic term, input variables based on the effects of rainfall and reservoir water level in landslide displacement were selected using grey relational analysis. Based on the results, the prediction precision of ELM is better than that of LS-SVM for predicting landslide displacement. The method for predicting landslide displacement could be applied by relevant authorities in making landslide emergency plans in the future.

Keywords】Landslide; Three Gorges Reservoir; Impoundment process; Displacement prediction