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Landslide Displacement Prediction Based on CEEMDAN Method and CNN–BiLSTM Model

Author

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  • Zian Lin

    (School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China)

  • Yuanfa Ji

    (Information and Communication School, Guilin University of Electronic Technology, Guilin 541004, China)

  • Xiyan Sun

    (Information and Communication School, Guilin University of Electronic Technology, Guilin 541004, China)

Abstract

Landslides are a typical geological disaster, and are a great challenge to land use management. However, the traditional landslide displacement model has the defect of ignoring random displacement. In order to solve this situation, this paper proposes a CNN–BiLSTM model that combines a convolutional neural network (CNN) model and a bidirectional long short-term memory network (BiLSTM) model. In this model, the CEEMDAN method is innovatively proposed to decompose landslide displacement. The GRA–MIC fusion correlation calculation method is used to select the factors influencing landslide displacement, and finally the CNN–BiLSTM model is used for prediction. The CNN–BiLSTM model was constructed to extract the temporal and spatial characteristics of data for landslide displacement prediction. Two new concepts that evaluate the state of a landslide and the trend of the landslide are proposed to improve the performance of the prediction model. Then, we discuss the prediction performance of the CNN–BiLSTM model under four different input conditions and compare it with seven other prediction models. The experimental prediction results show that the model proposed in this paper can be popularized and applied in areas with frequent landslides, and provide strong support for disaster prevention and reduction and land use management.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10071-:d:1179178
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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. 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.
    4. Mohammed A. Bou-Rabee & Muhammad Yasin Naz & Imad ED. Albalaa & Shaharin Anwar Sulaiman, 2022. "BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones," Energies, MDPI, vol. 15(6), pages 1-12, March.
    5. 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.
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    Cited by:

    1. Jeong-Cheol Kim & Sunmin Lee, 2023. "Comparative Study of Deep Neural Networks for Landslide Susceptibility Assessment: A Case Study of Pyeongchang-gun, South Korea," Sustainability, MDPI, vol. 16(1), pages 1-13, December.

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