@Article{cmes.2023.025993,
AUTHOR = {Feezan Ahmad, Xiaowei Tang, Jilei Hu, Mahmood Ahmad, Behrouz Gordan},
TITLE = {Improved Prediction of Slope Stability under Static and Dynamic Conditions Using Tree-Based Models},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {1},
PAGES = {455--487},
URL = {http://www.techscience.com/CMES/v137n1/52322},
ISSN = {1526-1506},
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 (Prec), recall (Rec), and F-score. The applications of the aforementioned methods for predicting
slope stability are compared to one another and recently established soft computing models in the literature. The
analysis of the Acc together with Mcc, and F-score for the slope stability in the test set demonstrates that the
RT achieved a better prediction performance with (Acc = 97.1429%, Mcc = 0.935, F-score for stable class = 0.979
and for unstable case F-score = 0.935) succeeded by the REP tree model with (Acc = 95.4286%, Mcc = 0.896,
F-score stable class = 0.967 and for unstable class F-score = 0.923) for the slope stability dataset The analysis of
performance measures for the slope stability dataset reveals that the RT model attains comparatively better and
reliable results and thus should be encouraged in further research.},
DOI = {10.32604/cmes.2023.025993}
}