Machine Learning Models for Slope Stability Classification of Circular Mode Failure: An Updated Database and Automated Machine Learning (AutoML) Approach

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 23; p. 9166
• Main Authors: Ma, Junwei, Jiang, Sheng, Liu, Zhiyang, Ren, Zhiyuan, Lei, Dongze, Tan, Chunhai, Guo, Haixiang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25.11.2022; MDPI
• Subjects: Algorithms; automated machine learning (AutoML); Automation; circular mode failure; Classification; Databases, Factual; Datasets; Equilibrium; Friction; Humans; hyperparameter tuning; Machine Learning; Neural networks; Performance evaluation; Risk assessment; ROC Curve; Shear strength; slope stability classification; stacked ensemble; Support vector machines; Sustainable development; Technology application; China; circular mode failure; slope stability classification; automated machine learning (AutoML); stacked ensemble; hyperparameter tuning

Slope failures lead to large casualties and catastrophic societal and economic consequences, thus potentially threatening access to sustainable development. Slope stability assessment, offering potential long-term benefits for sustainable development, remains a challenge for the practitioner and res...