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Predicting Factor of Safety of Slope Using an Improved Support Vector Machine Regression Model

Author

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  • Daxing Lei

    (School of Resources and Architectural Engineering, GanNan University of Science and Technology, Ganzhou 341000, China
    Key Laboratory of Mine Geological Disaster Prevention and Control and Ecological Restoration, Ganzhou 341000, China)

  • Yaoping Zhang

    (School of Resources and Architectural Engineering, GanNan University of Science and Technology, Ganzhou 341000, China
    Key Laboratory of Mine Geological Disaster Prevention and Control and Ecological Restoration, Ganzhou 341000, China)

  • Zhigang Lu

    (School of Resources and Architectural Engineering, GanNan University of Science and Technology, Ganzhou 341000, China
    Key Laboratory of Mine Geological Disaster Prevention and Control and Ecological Restoration, Ganzhou 341000, China)

  • Hang Lin

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Zheyuan Jiang

    (Jiangsu Key Laboratory of Urban Underground Engineering and Environmental Safety, Institute of Geotechnical Engineering, Southeast University, Nanjing 210096, China)

Abstract

To reduce the disasters caused by slope instability, this paper proposes a new machine learning (ML) model for slope stability prediction. This improved SVR model uses support vector machine regression (SVR) as the basic prediction tool and the grid search method with 5-fold cross-validation to optimize the hyperparameters to improve the prediction performance. Six features, namely, unit weight, cohesion, friction angle, slope angle, slope height, and pore pressure ratio, were taken as the input of the model, and the factor of safety was taken as the model output. Four statistical indicators, namely, the coefficient of determination (R 2 ), mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE), were introduced to assess the generalization performance of the model. Finally, the feature importance score of the features was clarified by calculating the importance of the six features and visualizing them. The results show that the model can well describe the nonlinear relationship between features and the factor of safety. The R 2 , MAPE, MAE, and RMSE of the testing dataset were 0.901, 7.41%, 0.082, and 0.133, respectively. Compared with other ML models, the improved SVR model had a better effect. The most sensitive feature was unit weight.

Suggested Citation

  • Daxing Lei & Yaoping Zhang & Zhigang Lu & Hang Lin & Zheyuan Jiang, 2024. "Predicting Factor of Safety of Slope Using an Improved Support Vector Machine Regression Model," Mathematics, MDPI, vol. 12(20), pages 1-17, October.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:20:p:3254-:d:1501047
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    References listed on IDEAS

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    1. 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.
    2. Shakti Suman & S. Z. Khan & S. K. Das & S. K. Chand, 2016. "Slope stability analysis using artificial intelligence 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. 84(2), pages 727-748, November.
    3. P. Lu & M. Rosenbaum, 2003. "Artificial Neural Networks and Grey Systems for the Prediction of Slope Stability," 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. 30(3), pages 383-398, November.
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