PREDICTION OF RESERVOIR LANDSLIDE DISPLACEMENTS CONSIDERING TIME LAG EFFECT—A CASE STUDY OF THE XINPU LANDSLIDE IN THE THREE GORGES RESERVOIR AREA, CHINA

Landslide instability in the reservoir area usually disproportionately impacts on the safety of life and production. Reliable landslide displacement prediction is of great importance for risk warning and disaster prevention and mitigation. However, conventional displacement prediction models fail to consider the lag effect of landslide deformation induced by the controlling factors(rainfall and reservoir water level), and to determine the lag time and the degree of influence. This paper takes the Xinpu landslide in the Three Gorges reservoir area as an example. The lag effect of landslide deformation induced by rainfall and reservoir water level at the hillslope scale is quantitatively described using Pearson correlation coefficient method based on the displacement monitoring and hydro-meteorological dataset in 2021. A novel landslide displacement prediction method considering the lag effect is presented using BP neural network model. The results show that the time lag effect of surface deformation induced by reservoir water level changes is obvious on the hillside scale. The lag time shows a pattern of increasing from near shore to far shore. The time lag effect induced by rainfall on surface deformation is weaker, and shows a pattern of low correlation with shorter lag time. Compared with the prediction model without considering the time lag effect, the fit of the model accounting for time lag effect is improved by 55.77%, and the root mean square error is reduced by 31.5%. The research results reveal the deformation mechanism of large-scale reservoir landslides to a certain extent, which can provide a reference for displacement prediction of similar landslides.