Prediction of Reservoir-Type Landslide Displacement Based on the Displacement Vector Angle and a Long Short-Term Memory Neural Network

Reservoir-type accumulation layer landslides have strong destructive force and complex displacement generation mechanisms. In this paper, the slope stability evaluation parameter of the displacement vector angle is introduced, and a rolling landslide displacement prediction method is proposed based on long short-term memory (LSTM) neural network. First, grey correlation analysis was employed to quantify the correlations between reservoir water level, rainfall patterns, cumulative displacement, and displacement vector angles with landslide displacement, thereby assessing the viability of incorporating displacement vector angles as predictive input features. Second, building upon the original study, historical displacement, displacement vector angle, and their combination are added as input features to assess the impact of various feature combinations on landslide displacement prediction outcomes. Thirdly, the LSTM model with different sliding window sizes is constructed to control different amounts of historical input data under different feature combinations. Finally, the impact of various feature combinations and varying amounts of historical inputs on landslide displacement prediction is assessed to identify the most effective prediction method. The method’s reliability is validated using actual monitoring data from the Bazimen landslide in the Three Gorges Reservoir area. The prediction results align with the monitoring data, confirming the feasibility of using the displacement vector angle as an input feature in the neural network for landslide displacement prediction.