Work

Prediction and pre-warning of step-like landslide displacement based on deep learning coupled with ICEEMDAN

Year: 2024

Type: article

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.

Source: Measurement

DOI: 10.1016/j.measurement.2024.116585

Authors: Zhou Zheng, Yanlong Li, Ye Zhang, Lifeng Wen, Xinyu Kang, Xinjian Sun

Institutions: Xi'an University of Technology, Qinghai University

Language: English

Topic: Landslides and related hazards

Subfield: Management, Monitoring, Policy and Law

Field: Environmental Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,650

Funders: National Natural Science Foundation of China, National Science Fund for Distinguished Young Scholars