Landslide Displacement Prediction Based on CEEMDAN Method and CNN–BiLSTM Model

Abstract

Landslides are a typical geological disaster, and are a great challenge to land use management. However, the traditional landslide displacement model has the defect of ignoring random displacement. In order to solve this situation, this paper proposes a CNN–BiLSTM model that combines a convolutional neural network (CNN) model and a bidirectional long short-term memory network (BiLSTM) model. In this model, the CEEMDAN method is innovatively proposed to decompose landslide displacement. The GRA–MIC fusion correlation calculation method is used to select the factors influencing landslide displacement, and finally the CNN–BiLSTM model is used for prediction. The CNN–BiLSTM model was constructed to extract the temporal and spatial characteristics of data for landslide displacement prediction. Two new concepts that evaluate the state of a landslide and the trend of the landslide are proposed to improve the performance of the prediction model. Then, we discuss the prediction performance of the CNN–BiLSTM model under four different input conditions and compare it with seven other prediction models. The experimental prediction results show that the model proposed in this paper can be popularized and applied in areas with frequent landslides, and provide strong support for disaster prevention and reduction and land use management.