A hybrid VMD-LSTM-SVR model for landslide prediction - Wang, Nianhong; Wang, Meijun; Zhang, Jun (Frontiers in Built Environment, 2025)
Landslides are one of the most prevalent natural geological disasters, causing significant economic losses, damaging public environments, and posing severe threats to human lives. Landslide displacement, influenced by various triggering factors, best reflects the landslide evolution process; when displacement reaches a certain threshold, a landslide may occur. Consequently, predicting landslide displacement has become a focal point in engineering research. This study employs the Long Short-Term Memory (LSTM) neural network and Support Vector Regression (SVR), combined with the Variational Mode Decomposition (VMD) algorithm, to construct predictive models. Initially, the VMD algorithm decomposes the landslide displacement time series into trend, periodic, and stochastic components. A novel Variational Mode Decomposition-Long Short-Term Memory (VMD-LSTM) hybrid model is then proposed for single-step landslide displacement prediction, followed by the application of a new Variational Mode Decomposition-Support Vector Regression (VMD-SVR) model for time series forecasting of landslide displacement. The results indicate that the VMD-SVR-LSTM model, with an RMSE of 0.0328 and an R2 of 0.8487, demonstrates the best predictive accuracy and fitting capability. The methodology proposed in this paper offers a viable approach for landslide disaster prevention and early warning systems.