Under the influences of the geological conditions of landslide and external periodic and random factors

the evolution process of the landslide has typical abrupt characteristics. The conventional deep learning model based on gating mechanism has insufficient ability to predict step mutation sequence

while by mining the potential information of time series data with different scales

multi-head self-attention can adaptively find the alteration trend of sequences and improve the prediction ability of landslide sequence. Based on variational mode decomposition

the cumulative displacement of landslide was decomposed into trend displacement

periodic displacement and stochastic displacement. Dynamic time warping algorithm was used to analyze the correlation between each component and influencing factors. These displacement components were dynamically predicted by a modified model integrated with long short-term memory (LSTM) neural network and multi-head self-attention mechanism

and the predicted values of each component were added to obtain the cumulative displacement prediction result. The monitoring point ZG118 of Baishuihe landslide in the Three Gorges Reservoir area was taken as an example to predict the cumulative displacement in this paper. The monitoring points ZG93 and XD01 were used for model adaptability verification. The results show that the new model can greatly improve the prediction accuracy for the step data mutation caused by the changes of rainfall and reservoir water level

providing a new idea for the prediction of landslide displacement in the Three Gorges Reservoir area.