Abstract: Addressing the issues of low accuracy and insufficient generalization in traditional landslide displacement pre diction methods,this paper proposes a novel model that integrates Variational Mode Decomposition (VMD) and deep learning based on the concept of "decomposition-prediction-reconstruction".The model identifies the influential features of landslide displacement through grey relational analysis and optimizes VMD using the Particle Swarm Optimization (PSO) algorithm, thereby decomposing landslide displacement into components with distinct physical meanings.To predict these components,the model employs polynomial curve fitting,Long Short-term Memory (LSTM) networks,and Temporal Convolutional Networks (TCN),depending on the time series characteristics of each component.The predictions of these components are then recon structed and aggregated to achieve an accurate prediction of landslide displacement.Using the data from the Bazimen landslide in Three Gorges Reservoir Region as an example,the model is quantitatively evaluated using metrics such as R2,MAE,and RMSE.The results demonstrate that the proposed landslide displacement prediction model achieves an accuracy of 98.6%,ef fectively extracting hidden information features from landslide displacement data,and has certain reference significance for get ting landslide displacement.The prediction outcomes vary significantly when different models are used for each component,in dicating that establishing specific prediction models based on the characteristics of each component can effectively improve the accuracy of landslide displacement predictions.Parameter sensitivity analysis reveals that the model achieves optimal accuracy when the input sequence length is 12.The model proposed in this article has good prediction accuracy and can provide a refer ence for the practical application of landslide disaster prevention and reduction projects.

Key words: landslide displacement,variational modal decomposition,deep learning,particle swarm optimization,grey cor relation