Abstract: Slope is a chaotic dynamic system influenced by various factors. It’s difficult to establish the deterministic equation of slope displacement since it is highly uncertain as a macro expression of the internal mechanical behavior of slope. Landslide is a genetic type of slope which has the same characteristics. Apart from groundwater, the major external motivation of landslide displacement, it is under the control of remedial measures after its treatment. Chaotic time series of landslide displacement and its influential factors could reflect the history of landslide displacement. According to the observed multivariable time series and the mapping relation between variables reflected by adopting RBF neural network, the displacement is predicted by reconstructing the dynamic system of landslide displacement. Results show that multivariable time series model could effectively predict landslide displacement, and the accuracy is higher than that of single-variable time series model; multivariable time series model is of clearer sense of the physical mechanics and reflects the real characteristics of deformation evolution.

Key words: prediction of landslide, chaos, multivariable time series, RBF neural network