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

Daxing Lei (), Yaoping Zhang, Zhigang Lu, Hang Lin and Zheyuan Jiang ()
Additional contact information
Daxing Lei: School of Resources and Architectural Engineering, GanNan University of Science and Technology, Ganzhou 341000, China
Yaoping Zhang: School of Resources and Architectural Engineering, GanNan University of Science and Technology, Ganzhou 341000, China
Zhigang Lu: School of Resources and Architectural Engineering, GanNan University of Science and Technology, 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

Mathematics, 2024, vol. 12, issue 20, 1-17

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.

Keywords: slope stability; factor of safety; improved prediction; machine learning; support vector machine regression (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/20/3254/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/20/3254/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:20:p:3254-:d:1501047

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().