Prediction and pre-warning of step-like landslide displacement based on deep learning coupled with ICEEMDAN
Zheng Zhou, Yanlong Li, Ye Zhang, Lifeng Wen, Xinyu Kang, Xinjian Sun
Abstract
Characterized by abrupt and creep displacements, prediction and pre-warning of step-like landslides pose significant challenges. To address this issue, we proposed a novel approach that integrated landslide displacement prediction with pre-warning systems. This method involved decomposing the landslide displacement into trend and periodic components to enhance accuracy. A cubic polynomial combined with a Dense Convolutional Network and a Long Short-Term Memory Network was adopted to predict the trend and periodic displacements, which were then combined for the final estimation. Additionally, a pre-warning index system was developed, considering the cumulative landslide displacement effects, including revised tangential angle thresholds, tangential angle variations, and duration considerations. The effectiveness of this model and the pre-warning index were validated through an analysis of the Baishuihe and Bazimen landslides. The results indicated that the proposed model accurately simulated the displacements and provided reliable landslide pre-warning, offering a viable solution for predicting and warning about step-like landslides.