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Prediction of Slope Safety Factor Based on Attention Mechanism-Enhanced CNN-GRU

Author

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  • Qi Da

    (School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China)

  • Ying Chen

    (School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China)

  • Bing Dai

    (School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China)

  • Danli Li

    (School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China)

  • Longqiang Fan

    (School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China)

Abstract

This paper proposes a new method for predicting slope safety factors that combines convolutional neural networks (CNNs), gated recurrent units (GRUs), and attention mechanisms. This method can better capture long-term dependencies, enhance the ability to model sequential data, and reduce the dependence on noisy data, thereby reducing the risk of overfitting. The goal is to improve the accuracy of slope safety factor prediction, detect potential slope stability issues in a timely manner, and take corresponding preventive and control measures to ensure the long-term stability and safety of infrastructure and promote sustainable development. The Pearson correlation coefficient is used to analyze the relationship between the target safety factor and the collected parameters. A one-dimensional CNN layer is used to extract high-dimensional features from the input data, and then a GRU layer is used to capture the correlation between parameters in the sequence. Finally, an attention mechanism is introduced to optimize the weights of the GRU output, enhance the influence of key information, and optimize the overall prediction model. The performance of the proposed model is evaluated using metrics such as the mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), root-mean-square error (RMSE), and R 2 . The results show that the CNN-GRU-SE model outperforms the GRU, CNN, and CNN-GRU models in terms of prediction accuracy for slope safety factors, with improvements of 4%, 2%, and 1%, respectively. Overall, the research in this paper makes valuable contributions to the field of slope safety factor prediction, and the proposed method also has the potential to be extended to other time-series prediction fields, providing support for a wide range of engineering applications and further promoting the realization of sustainable development.

Suggested Citation

  • Qi Da & Ying Chen & Bing Dai & Danli Li & Longqiang Fan, 2024. "Prediction of Slope Safety Factor Based on Attention Mechanism-Enhanced CNN-GRU," Sustainability, MDPI, vol. 16(15), pages 1-23, July.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:15:p:6333-:d:1441888
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    References listed on IDEAS

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    1. Arsalan Mahmoodzadeh & Mokhtar Mohammadi & Hunar Farid Hama Ali & Hawkar Hashim Ibrahim & Sazan Nariman Abdulhamid & Hamid Reza Nejati, 2022. "Prediction of safety factors for slope stability: comparison of machine learning techniques," 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. 111(2), pages 1771-1799, March.
    2. Zaobao Liu & Jianfu Shao & Weiya Xu & Hongjie Chen & Yu Zhang, 2014. "An extreme learning machine approach for slope stability evaluation and prediction," 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. 73(2), pages 787-804, September.
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