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Bayesian ensemble learning and Shapley additive explanations for fast estimation of slope stability with a physics-informed database

Author

Listed:
  • Dongze Lei

    (China University of Geosciences
    China University of Geosciences)

  • Junwei Ma

    (China University of Geosciences
    China University of Geosciences)

  • Guangcheng Zhang

    (China University of Geosciences
    Hubei Key Laboratory of Operation Safety of High Dam and Large Reservoir)

  • Yankun Wang

    (Yangtze University)

  • Xin Deng

    (China University of Geosciences
    China University of Geosciences)

  • Jiayu Liu

    (China University of Geosciences
    China University of Geosciences)

Abstract

Slope failures present substantial threats to public safety and economic losses. However, it remains challenging to achieve satisfactory performance due to insufficient datasets with machine learning (ML)-based slope stability assessment. In this study, an expanded physics-informed dataset was constructed by integrating historical case studies with data derived from nonintrusive stochastic analysis. The Bayesian ensemble learning model was employed to enhance prediction accuracy, with the Shapley additive explanations method employed to elucidate the contribution of each input variable. The proposed method displayed satisfactory performance, achieving an area under the curve of 0.9973, accuracy of 0.9727, and F1-score of 0.9729, surpassing the compared ML methods. Its robustness and generalization capabilities were confirmed through evaluations on diverse datasets and random seeds. Furthermore, a user-friendly graphical user interface was created for fast estimation of slope stability (FESS) using the trained prediction model. The performance of FESS was validated on a series of examples including the Australian Association for Computer-Aided Design referenced slope example EX1 and 77 in situ cases. This tool offers practitioners a high-performance solution, significantly reducing the effort required for slope stability assessments.

Suggested Citation

  • Dongze Lei & Junwei Ma & Guangcheng Zhang & Yankun Wang & Xin Deng & Jiayu Liu, 2025. "Bayesian ensemble learning and Shapley additive explanations for fast estimation of slope stability with a physics-informed database," 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. 121(3), pages 2941-2970, February.
  • Handle: RePEc:spr:nathaz:v:121:y:2025:i:3:d:10.1007_s11069-024-06917-2
    DOI: 10.1007/s11069-024-06917-2
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    References listed on IDEAS

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    1. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    2. Yukun Yang & Wei Zhou & Izhar Mithal Jiskani & Xiang Lu & Zhiming Wang & Boyu Luan, 2023. "Slope Stability Prediction Method Based on Intelligent Optimization and Machine Learning Algorithms," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    3. Talas Fikret Kurnaz & Caner Erden & Uğur Dağdeviren & Alparslan Serhat Demir & Abdullah Hulusi Kökçam, 2024. "Comparison of machine learning algorithms for slope stability prediction using an automated machine learning approach," 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. 120(8), pages 6991-7014, June.
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