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Prediction of Slope Stability using Naive Bayes Classifier

  • Probabilistic Learning for Decision-making on Civil Infrastructures
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KSCE Journal of Civil Engineering Aims and scope

Abstract

Slope stability prediction is of primary concern in identifying terrain that is susceptible to landslides and mitigating the damages caused by landslides. In this study, a Naive Bayes Classifier (NBC) was employed to predict slope stability for a slope subjected to circular failures, based on six input factors: slope height (H), slope angle (α), cohesion (c), friction angle (φ), unit weight (γ), and pore pressure ratio (r u ). An expectation maximization algorithm was used to perform parameter learning for the NBC with an incomplete data set of 69 slope cases. The model validation with 13 new cases shows that, when compared to the existing empirical approach, the proposed NBC model yields better performance in terms of both accuracy and applicability (i.e., the NBC allows us to determine the probability of slope stability based on any subset of the six input factors).

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Feng, X., Li, S., Yuan, C. et al. Prediction of Slope Stability using Naive Bayes Classifier. KSCE J Civ Eng 22, 941–950 (2018). https://doi.org/10.1007/s12205-018-1337-3

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  • DOI: https://doi.org/10.1007/s12205-018-1337-3

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