Open Access

ARTICLE

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

by Feezan Ahmad¹, Xiaowei Tang¹, Jilei Hu^2,*, Mahmood Ahmad^3,4, Behrouz Gordan⁵

1 State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, China
2 Key Laboratory of Geological Hazards on Three Gorges Reservoir Area, Ministry of Education, China Three Gorges University, Yichang, 443002, China
3 Department of Civil Engineering, Faculty of Engineering, International Islamic University Malaysia, Jalan Gombak, Selangor, 50728, Malaysia
4 Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu, 28100, Pakistan
5 Faculty of Civil Engineering, Islamic Azad University, Varamin Pishva Branch, Tehran, 15914, Iran

* Corresponding Author: Jilei Hu. Email:

(This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)

Computer Modeling in Engineering & Sciences 2023, 137(1), 455-487. https://doi.org/10.32604/cmes.2023.025993

Received 09 August 2022; Accepted 23 December 2022; Issue published 23 April 2023

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.

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