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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