Mechanism guided forecasting model for landslide displacement prediction: case study for Baishuihe Landslide

Landslides often exhibit step-like deformation and abrupt displacement increases, making accurate early warning highly challenging. Understanding the evolutionary mechanisms and extracting effective predictive features from dynamic environmental factors are critical for improving forecasting performance. This study proposes a hybrid forecasting model based on a deep understanding of landslide evolution mechanisms. First, Variational Mode Decomposition (VMD) was employed to analyze the dynamic response relationship between displacement and influencing factors (e.g., rainfall and reservoir water level). Then, Double Exponential Smoothing (DES) was applied to decompose cumulative displacement into trend and periodic components, enabling the identification of effective external input features. Finally, the Informer model, integrated with a multi-head attention mechanism and pooling layers, was developed to capture critical periodic information from time-series data. The proposed model was validated using six consecutive years of monitoring data from the Baishuihe Landslide in the Three Gorges Reservoir Area. The results show that the model achieves high prediction accuracy, significantly outperforming mainstream machine learning models with superior error control.