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Research on SSA-LSTM-Based Slope Monitoring and Early Warning Model

Author

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  • Shasha Yang

    (College of Civil Engineering, Xijing University, Xi’an 710123, China
    Shaanxi Key Laboratory of Safety and Durability of Concrete Structures, Xi’an 710123, China)

  • Anjie Jin

    (College of Civil Engineering, Xijing University, Xi’an 710123, China
    Shaanxi Key Laboratory of Safety and Durability of Concrete Structures, Xi’an 710123, China)

  • Wen Nie

    (Quanzhou Equipment Manufacturing Research Center, Haixi Research Institute, Chinese Academy of Sciences, Quanzhou 362000, China)

  • Cong Liu

    (College of Civil Engineering, Xijing University, Xi’an 710123, China
    Shaanxi Key Laboratory of Safety and Durability of Concrete Structures, Xi’an 710123, China)

  • Yu Li

    (College of Civil Engineering, Xijing University, Xi’an 710123, China
    Shaanxi Key Laboratory of Safety and Durability of Concrete Structures, Xi’an 710123, China)

Abstract

For geological disasters such as landslides, active prevention and early avoidance are the main measures to avoid major losses. Therefore, landslide early warning is an effective means to prevent the occurrence of landslide disasters. In this paper, based on geological survey and monitoring data, a landslide monitoring and early warning model based on SSA-LSTM is established for the landslide in Yaoshan Village, Xiping Town, Anxi County, Fujian Province, China. In the early warning model, the hyper parameters of the LSTM neural network are optimized using the SSA algorithm in order to achieve high-accuracy displacement prediction of the LSTM displacement prediction model, and are compared with the unoptimized LSTM, and the results show that the prediction effect of the optimized SSA-LSTM model is significantly improved. Since landslide monitoring and early warning is a long-term work, the model trained by the traditional offline learning method will inevitably have distortion of the prediction effect as the monitoring time becomes longer, so the online migration learning method is used to update the displacement prediction model and combine with the tangent angle model to quantify the warning level. The monitoring and early warning model put forth in this research can be used as a guide for landslide disaster early warning.

Suggested Citation

  • Shasha Yang & Anjie Jin & Wen Nie & Cong Liu & Yu Li, 2022. "Research on SSA-LSTM-Based Slope Monitoring and Early Warning Model," Sustainability, MDPI, vol. 14(16), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10246-:d:891067
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    References listed on IDEAS

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