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).


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Prediction of Slope Stability using Naive Bayes Classifier


    Contributors:
    Feng, Xianda (author) / Li, Shuchen (author) / Yuan, Chao (author) / Zeng, Peng (author) / Sun, Yang (author)

    Published in:

    Publication date :

    2018-03-01


    Size :

    10 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Prediction of Slope Stability using Naive Bayes Classifier

    Feng, Xianda / Li, Shuchen / Yuan, Chao et al. | Online Contents | 2018


    Anomaly Detection in Flight Data Using the Naïve Bayes Classifier

    Jalawkhan, Murtaja S. / Mustafa, Tareef K. | IEEE | 2021


    Analysis of user emotions and opinion using Multinomial Naive Bayes Classifier

    Surya, Prabha PM / Seetha, Lakshmi V / Subbulakshmi, B | IEEE | 2019



    Instance Cloning Local Naive Bayes

    Jiang, L. / Zhang, H. / Su, J. et al. | British Library Conference Proceedings | 2005