The monitoring data of landslide deformation are characterized by non-smooth, nonlinear and random changes, and the cumulative changes of the monitored objects have both monotonous growth trends and short-term fluctuations. The GM(1,1) model can get better results only when the data series are monotonous. Due to the limitations of the model, the prediction accuracy of the GM(1,1) model is limited to a certain extent. An improved algorithm based on the GM(1,1) model and the empirical mode decomposition (EMD-GM(1,1) model) for deformation predict...
