Slope Stability Analysis Using Rf, Gbm, Cart, Bt and Xgboost

• Published in: Geotechnical and geological engineering Vol. 39; no. 5; pp. 3741 - 3752
• Main Authors: Bharti, Jayanti Prabha, Mishra, Pratishtha, moorthy, Usha, Sathishkumar, V. E., Cho, Yongyun, Samui, Pijush
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2021; Springer Nature B.V
• Subjects: Algorithms; Civil Engineering; Classification; Computer applications; Earth and Environmental Science; Earth Sciences; Failure analysis; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrostatic pressure; Internal friction; Mathematical models; Mining engineering; Original Paper; Pore pressure; Pore water; Pore water pressure; Pressure ratio; Regression analysis; Risk reduction; Scientific papers; Slope stability; Stability analysis; Statistical analysis; Statistical models; Terrestrial Pollution; Waste Management/Waste Technology; Water pressure; BT; Classification trees; GBM; Slope Stability Classification; RF; XGBoost; CART
• ISSN: 0960-3182; 1573-1529
• DOI: 10.1007/s10706-021-01721-2

Slopes in geotechnical and mining engineering are the most crucial geo-structure. Predicting or forecasting the stability or instability of the slope and then classifying the slope accordingly helps in mitigating the risks and enhancing the design by maximizing the safety. Computing techniques have overpowered the analytical and statistical models used for predicting the stability of the slopes. To reduce the uncertainties and ambiguity of the previously used models, lately, researchers have come up with the novel techniques for Slope Stability Classification (SSC) which are Random Forest, Gradient Boosting Machine, Extreme Gradient Boosting, Boosted Trees and Classification and Regression Trees. These computational algorithms are employed in this research paper and the slope details are taken from a literature i.e. 221 input datasets are used and slopes are classified accordingly using the mentioned models. The relation between the inputs such as height (H), slope angle (β), cohesion (c), pore water pressure ratio (ru), unit weight (γ), angle of internal friction (φ) and slope stability (output) is established and slopes are categorized according to their failure and stability. Performance analysis is done thereafter to analyses and compare different models and let the readers and researchers know that which model sufficed and fitted best to the study.