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    Improved Prediction of Slope Stability under Static and Dynamic Conditions Using Tree-Based Models

    Feezan Ahmad1, Xiaowei Tang1, Jilei Hu2,*, Mahmood Ahmad3,4, Behrouz Gordan5

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 455-487, 2023, DOI:10.32604/cmes.2023.025993

    Abstract Slope stability prediction plays a significant role in landslide disaster prevention and mitigation. This paper’s reduced error pruning (REP) tree and random tree (RT) models are developed for slope stability evaluation and meeting the high precision and rapidity requirements in slope engineering. The data set of this study includes five parameters, namely slope height, slope angle, cohesion, internal friction angle, and peak ground acceleration. The available data is split into two categories: training (75%) and test (25%) sets. The output of the RT and REP tree models is evaluated using performance measures including accuracy (Acc), Matthews correlation coefficient (Mcc), precision… More >

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