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

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.

Automated summary

This paper develops the REP tree and RT models for slope stability evaluation in slope engineering. The evaluation of these models shows that the RT model outperforms the REP tree model in terms of prediction accuracy and other performance measures. Therefore, the RT model should be encouraged for further research.