A new method for displacement prediction of "step-like" landslides based on VMD-FOA-SVR model

Landslide prediction is important for mitigating geohazards but is very challenging. There are many landslides in the Three Gorges Reservoir area, which is in the middle and upper reaches of the Yangtze River Basin in China. For the riverside landslide, the fluctuation of reservoir water level and rainfall are the main external triggering factors controlling the deformation of landslides. In this paper, the Bazimen landslide in the Three Gorges Reservoir area, which has a typical "step-like" behavior, was taken as an example, and a new method of landslide displacement prediction (VMD-FOA-SVR) was proposed. First, 9 triggering factors including the fluctuation of reservoir level and rainfall were extracted. Then, the displacement of ZG110 and triggering factors are decomposed by Variational Mode Decomposition (VMD) based on the time series analysis of landslides. Last, the trend term displacement was trained and predicted by one-dimensional cubic subsection functions, and FOA-VMD models were utilized to train and predict the periodic and random term. Results show that the prediction model established in this paper has achieved good accuracy in the total displacement of ZG110, which can effectively improve the prediction accuracy, and has high practicability and application value in the study of landslide displacement prediction.