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Landslide Displacement Prediction Based on Time Series Analysis and Double-BiLSTM Model

Author

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  • Zian Lin

    (School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
    Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China)

  • Xiyan Sun

    (Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China
    Information and Communication School, Guilin University of Electronic Technology, Guilin 541004, China
    GUET-Nanning E-Tech Research Institute Co., Ltd., Nanning 530031, China)

  • Yuanfa Ji

    (Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China)

Abstract

In recent years, machine learning models facilitated notable performance improvement in landslide displacement prediction. However, most existing prediction models which ignore landslide data at each time can provide a different value and meaning. To analyze and predict landslide displacement better, we propose a dynamic landslide displacement prediction model based on time series analysis and a double-bidirectional long short term memory (Double-BiLSTM) model. First, the cumulative landslide displacement is decomposed into trend and periodic displacement components according to time series analysis via the exponentially weighted moving average (EWMA) method. We consider that trend displacement is mainly influenced by landslide factors, and we apply a BiLSTM model to predict landslide trend displacement. This paper analyzes the internal relationship between rainfall, reservoir level and landslide periodic displacement. We adopt the maximum information coefficient (MIC) method to calculate the correlation between influencing factors and periodic displacement. We employ the BiLSTM model for periodic displacement prediction. Finally, the model is validated against data pertaining to the Baishuihe landslide in the Three Gorges, China. The experimental results and evaluation indicators demonstrate that this method achieves a better prediction performance than the classical prediction methods, and landslide displacement can be effectively predicted.

Suggested Citation

  • Zian Lin & Xiyan Sun & Yuanfa Ji, 2022. "Landslide Displacement Prediction Based on Time Series Analysis and Double-BiLSTM Model," IJERPH, MDPI, vol. 19(4), pages 1-23, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:4:p:2077-:d:748140
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    References listed on IDEAS

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    1. Peng, Tian & Zhang, Chu & Zhou, Jianzhong & Nazir, Muhammad Shahzad, 2021. "An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting," Energy, Elsevier, vol. 221(C).
    2. Xiuzhen Li & Jiming Kong & Zhenyu Wang, 2012. "Landslide displacement prediction based on combining method with optimal weight," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 61(2), pages 635-646, March.
    3. Yong-gang Zhang & Jun Tang & Zheng-ying He & Junkun Tan & Chao Li, 2021. "A novel displacement prediction method using gated recurrent unit model with time series analysis in the Erdaohe landslide," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 783-813, January.
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    Cited by:

    1. Zian Lin & Yuanfa Ji & Xiyan Sun, 2023. "Landslide Displacement Prediction Based on CEEMDAN Method and CNN–BiLSTM Model," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    2. Zian Lin & Yuanfa Ji & Xiyan Sun, 2023. "Advance Landslide Prediction and Warning Model Based on Stacking Fusion Algorithm," Mathematics, MDPI, vol. 11(13), pages 1-20, June.

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