Application of Multivariable Time Series Based on RBF Neural Network in Prediction of Landslide Displacement

Landslide is a kind of genetic type of slope and has the same characteristics with slope. The major external motivation factor of landslide displacement is groundwater and it is under the control of remedial measures at the same time after its remediation. Chaotic time series of landslide displacement and its influential factors could reflect the history of landslide displacement of dynamic system, the displacement could be predicted by reconstructing landslide displacement of dynamic system according to the observation of multivariate time series and adopting RBF neural network to reflect relationship between variables. Comparative analysis of the results from the forecast show that: multivariable time series model can predict landslide displacement effectively, and the forecast accuracy is higher than the accuracy of a single variable time series model; multivariable time series model is of clearer sense of the physical mechanics and reflects the real evolution of deformation characteristics more effective.