Integrating Time Series Decomposition and Deep Learning: An STL-TCN-Transformer Framework for Landslide Displacement Prediction

Accurate prediction of landslide displacement is crucial for disaster prevention and mitigation. This study proposes an STL-TCN-Transformer model that combines time series decomposition with deep learning to predict cumulative displacement. Using monitoring data from the Baishuihe landslide, the displacement sequence was decomposed into trend, periodic, and residual components using the STL method. The trend component, determined by geotechnical properties, was predicted using a univariate TCN-Transformer, while the periodic and residual components, influenced by rainfall and reservoir water levels, were analyzed for nonlinear correlations using the Spearman method and predicted with a multivariate TCN-Transformer. The proposed model achieved superior performance, with R2 of 0.993, RMSE of 7.82 mm, and MAE of 5.82 mm, significantly outperforming EMD-LSTM, EEMD-RNN, and VMD-BiLSTM models in all metrics. These findings demonstrate the ability of the STL-TCN-Transformer to effectively capture the dynamics of landslide displacement, offering a reliable approach for landslide monitoring and early warning systems.