Prediction of Landslide Displacement Based on the Combined VMD-Stacked LSTM-TAR Model

A dynamic prediction model of landslide displacement based on VMD-SSO-LSTM approach

Addressing the limitations of existing landslide displacement prediction models in capturing the dynamic characteristics of data changes, this study introduces a novel dynamic displacement prediction model for landslides. The proposed method combines Variational Mode Decomposition (VMD) with Sparrow Search Optimization (SSO) and Long Short-Term Memory (LSTM) techniques to formulate a comprehensive VMD-SSO-LSTM model. Through the application of VMD, the method dissects cumulative displacement and rainfall data, thereby extracting distinct components such as trend, periodicity, and fluctuation components for displacement, as well as low-frequency and high-frequency components for rainfall. Furthermore, leveraging Gray Correlational Analysis, the interrelationships between the periodic component of displacement and the low-frequency component of rainfall, as well as the fluctuation component of displacement and the high-frequency component of rainfall, are established. Building upon this foundation, the SSO-LSTM model dynamically predicts the interrelated displacement components, synthesizing the predicted values of each component to generate real-time dynamic forecasts. Simulation results underscore the effectiveness of the proposed VMD-SSO-LSTM model, indicating root-mean-square error (RMSE) and mean absolute percentage error (MAPE) values of 1.2329 mm and 0.1624%, respectively, along with a goodness of fit (R2) of 0.9969. In comparison to both back propagation (BP) prediction model and LSTM prediction model, the VMD-SSO-LSTM model exhibits heightened predictive accuracy.

Landslide Displacement Prediction via Attentive Graph Neural Network

Landslides are among the most common geological hazards that result in considerable human and economic losses globally. Researchers have put great efforts into addressing the landslide prediction problem for decades. Previous methods either focus on analyzing the landslide inventory maps obtained from aerial photography and satellite images or propose machine learning models—trained on historical land deformation data—to predict future displacement and sedimentation. However, existing approaches generally fail to capture complex spatial deformations and their inter-dependencies in different areas. This work presents a novel landslide prediction model based on graph neural networks, which utilizes graph convolutions to aggregate spatial correlations among different monitored locations. Besides, we introduce a novel locally historical transformer network to capture dynamic spatio-temporal relations and predict the surface deformation. We conduct extensive experiments on real-world data and demonstrate that our model significantly outperforms state-of-the-art approaches in terms of prediction accuracy and model interpretations.