A high-precision displacement prediction model for landslide geological hazards based on APSO-SVR-LSTM combination

The development of a high-precision displacement prediction model for landslide geological hazards is crucial for the early warning of such disasters. Landslide deformation typically exhibits a step-like curve pattern with implicit periodicity. Therefore, taking the Xintan landslide in the Baishui River of the Three Gorges Reservoir Area as a case study, this study proposes a novel displacement prediction approach that integrates the Adaptive Particle Swarm Optimization (APSO), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) network. The APSO is employed to optimize the hyperparameters of the SVR model, ensuring an optimal parameter combination. Subsequently, the Grey Wolf Optimizer is utilized to assign weights to the APSO-SVR and LSTM models, establishing an optimal hybrid model with an optimal weight ratio. Using the Baishui River landslide as the research object, cumulative displacement, rainfall, and reservoir water level are selected as influencing factors of periodic displacement for model training and validation. The results demonstrate that, in predicting the periodic displacement of the Baishui River landslide, the proposed APSO-SVR-LSTM hybrid model outperforms individual models in terms of both prediction accuracy and stability.