Taking the Bazimen landslide in the Three Gorges reservoir area as an example

a new landslide displacement prediction method is proposed to solve the problems of the static machine learning model in the periodic displacement prediction and the difficulty in the high-frequency random displacement prediction. Based on the idea of time series decomposition

particle swarm optimization (PSO) is used to optimize the parameters of variational mode decomposition (VMD)

and the displacement time series is decomposed into trend term

periodic term and random term. The trend term is mainly affected by the internal factors of landslide

and the Fourier curve is used to fit and predict. The periodic term is caused by external factors. The causes are analyzed based on Granger causality test

and a nonlinear autoregressive neural network with exogenous inputs (NARX) with high sensitivity to the historical state of time series is introduced for prediction. The random term frequency is high and the influencing factors cannot be determined

thus one-dimensional gated recurrent unit (GRU) is used for prediction. Finally

the predicted displacement of each component is superimposed and reconstructed to realize the prediction of landslide cumulative displacement. The results show that the (PSO-VMD)-NARX-GRU landslide displacement dynamic prediction model has higher accuracy

and the prediction accuracy of each displacement component is obviously higher than that of BP neural network

support vector machine (SVM) and conventional autoregressive model ARIMA in static models

which can provide reference for step landslide displacement prediction.