Prediction of safety factors for slope stability: comparison of machine learning techniques

Arsalan Mahmoodzadeh (), Mokhtar Mohammadi (), Hunar Farid Hama Ali (), Hawkar Hashim Ibrahim (), Sazan Nariman Abdulhamid () and Hamid Reza Nejati ()
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Arsalan Mahmoodzadeh: University of Halabja
Mokhtar Mohammadi: Lebanese French University
Hunar Farid Hama Ali: University of Halabja
Hawkar Hashim Ibrahim: Salahaddin University-Erbil
Sazan Nariman Abdulhamid: Salahaddin University-Erbil
Hamid Reza Nejati: Tarbiat Modares University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 111, issue 2, No 26, 1799 pages

Abstract: Abstract Because of the disasters associated with slope failure, the analysis and forecasting of slope stability for geotechnical engineers are crucial. In this work, in order to forecast the factor of safety (FOS) of the slopes, six machine learning techniques of Gaussian process regression (GPR), support vector regression, decision trees, long-short term memory, deep neural networks, and K-nearest neighbors were performed. A total of 327 slope cases in Iran with various geometric and shear strength parameters analyzed by PLAXIS software to evaluate their FOS were employed in the models. The K-fold (K = 5) cross-validation (CV) method was applied to evaluate the performance of models’ prediction. Finally, all the models produced acceptable results and almost close to each other. However, the GPR model with R2 = 0.8139, RMSE = 0.160893, and MAPE = 7.209772% was the most accurate model to predict slope stability. Also, the backward selection method was applied to evaluate the contribution of each parameter in the prediction problem. The results showed that all the features considered in this study have significant contributions to slope stability. However, features φ (friction angle) and γ (unit weight) were the most effective and least effective parameters on slope stability, respectively.

Keywords: Slope stability; Factor of safety; Machine learning; PLAXIS; Feature selection (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-021-05115-8

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