A Landslide Displacement Prediction Model Based on the ICEEMDAN Method and the TCN–BiLSTM Combined Neural Network
Author:
Lin Qinyue1, Yang Zeping1, Huang Jie1ORCID, Deng Ju1, Chen Li1, Zhang Yiru1
Affiliation:
1. Department of Civil and Architectural Engineering, East China University of Technology, Nanchang 330013, China
Abstract
Influenced by autochthonous geological conditions and external environmental changes, the evolution of landslides is mostly nonlinear. This article proposes a combined neural network prediction model that combines a temporal convolutional neural network (TCN) and a bidirectional long short-term memory neural network (BiLSTM) to address the shortcomings of traditional recurrent neural networks in predicting displacement-fluctuation-type landslides. Based on the idea of time series decomposition, the improved complete ensemble empirical mode decomposition with an adaptive noise method (ICEEMDAN) was used to decompose displacement time series data into trend and fluctuation terms. Trend displacement is mainly influenced by the internal geological conditions of a landslide, and polynomial fitting is used to determine the future trend displacement; The displacement of the fluctuation term is mainly influenced by the external environment of landslides. This article selects three types of landslide-influencing factors: rainfall, groundwater level elevation, and the historical displacement of landslides. It uses a combination of gray correlation (GRG) and mutual information (MIC) correlation modules for feature screening. Then, TCN is used to extract landslide characteristic factors, and BiLSTM captures the relationship between features and displacement to achieve the prediction of wave term displacement. Finally, the trend term and fluctuation term displacement prediction values are reconstructed to obtain the total displacement prediction value. The results indicate that the ICEEMDAN–TCN–BiLSTM model proposed in this article can accurately predict landslide displacement and has high engineering application value, which is helpful for planning and constructing landslide disaster prevention projects.
Funder
East China University of Technology Graduate Innovation Fund
Subject
Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry
Reference59 articles.
1. Research on dynamic prediction model of landslide displacement based on particle swarm optimization-variational mode decomposition, nonlinear autoregressive neural network with exogenous inputs and gated recurrent unit;Jing;Rock Soil Mech.,2022 2. Displacement prediction of baishuihe landslide based on time series and PSO-SVR model;Zhang;Chin. J. Rock Mech. Eng.,2015 3. Zhang, J., Tang, H., Wen, T., Ma, J., Tan, Q., Xia, D., Liu, X., and Zhang, Y. (2020). A Hybrid Landslide Displacement Prediction Method Based on CEEMD and DTW-ACO-SVR—Cases Studied in the Three Gorges Reservoir Area. Sensors, 20. 4. A hybrid interval displacement forecasting model for reservoir colluvial landslides with step-like deformation characteristics considering dynamic switching of deformation states;Li;Stoch. Environ. Res. Risk Assess.,2021 5. Haghshenas, S.S., Haghshenas, S.S., Geem, Z.W., Kim, T.-H., Mikaeil, R., Pugliese, L., and Troncone, A. (2021). Application of harmony search algorithm to slope stability analysis. Land, 10.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|