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Landslide Displacement Prediction Based on Time-Frequency Analysis and LMD-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)

  • Yuanfa Ji

    (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)

  • Weibin Liang

    (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)

  • 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)

Abstract

In landslide displacement prediction, random factors that would affect the performance of prediction are usually ignored by using a time series analysis method. In order to solve this problem, in this paper, a landslide displacement prediction model, the local mean decomposition-bidirectional long short-term memory (LMD-BiLSTM), is proposed based on the time-frequency analysis method. The model uses the local mean decomposition (LMD) algorithm to decompose landslide displacement and obtains several subsequences of landslide displacement with different frequencies. This paper analyzes the internal relationship between the landslide displacement and rainfall, reservoir water level, and landslide state. The maximum information coefficient (MIC) algorithm is used to calculate the intrinsic correlation between each subsequence of landslide displacement and rainfall, reservoir water level, and landslide state. Subsequences of influential factors with high correlation are selected as input variables of the bidirectional long short-term memory (BiLSTM) model to predict each subsequence. Finally, the predicted results of each of the subsequences are added to obtain the final predicted displacement. The proposed LMD-BiLSTM model effectiveness is verified based on the Baishuihe landslide. The prediction results and evaluation indexes show that the model can accurately predict landslide displacement.

Suggested Citation

  • Zian Lin & Yuanfa Ji & Weibin Liang & Xiyan Sun, 2022. "Landslide Displacement Prediction Based on Time-Frequency Analysis and LMD-BiLSTM Model," Mathematics, MDPI, vol. 10(13), pages 1-19, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2203-:d:846800
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    References listed on IDEAS

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    1. Li, Shaohong & Wu, Na, 2021. "A new grey prediction model and its application in landslide displacement prediction," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    2. 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.
    3. Yin, Juan & Deng, Zhen & Ines, Amor V.M. & Wu, Junbin & Rasu, Eeswaran, 2020. "Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)," Agricultural Water Management, Elsevier, vol. 242(C).
    4. Ngoc-Lan Huynh, Anh & Deo, Ravinesh C. & Ali, Mumtaz & Abdulla, Shahab & Raj, Nawin, 2021. "Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition," Applied Energy, Elsevier, vol. 298(C).
    5. 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.
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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.

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