Dynamic intelligent prediction approach for landslide displacement based on biological growth models and CNN-LSTM

Influenced by complex external factors, the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity. It is difficult for existing methods, including Regression models and Neural network models, to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics. This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model (BG), Convolutional Neural Network (CNN), and Long Short-Term Memory Network (LSTM). This prediction approach improves three different biological growth models, thereby effectively extracting landslide creep characteristic parameters. Simultaneously, it integrates external factors (rainfall and reservoir water level) to construct an internal and external comprehensive dataset for data augmentation, which is input into the improved CNN-LSTM model. Thereafter, harnessing the robust feature extraction capabilities and spatial translation invariance of CNN, the model autonomously captures short-term local fluctuation characteristics of landslide displacement, and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance. An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy, excellent generalization capabilities when dealing with various types of landslides. The research provides an innovative approach to achieving the whole-process, real-time, high-precision displacement predictions for multi-characteristic coupled landslides.