Bayesian ensemble learning and Shapley additive explanations for fast estimation of slope stability with a physics-informed database

Dongze Lei, Junwei Ma (), Guangcheng Zhang, Yankun Wang, Xin Deng and Jiayu Liu
Additional contact information
Dongze Lei: China University of Geosciences
Junwei Ma: China University of Geosciences
Guangcheng Zhang: China University of Geosciences
Yankun Wang: Yangtze University
Xin Deng: China University of Geosciences
Jiayu Liu: China University of Geosciences

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 3, No 22, 2970 pages

Abstract: Abstract Slope failures present substantial threats to public safety and economic losses. However, it remains challenging to achieve satisfactory performance due to insufficient datasets with machine learning (ML)-based slope stability assessment. In this study, an expanded physics-informed dataset was constructed by integrating historical case studies with data derived from nonintrusive stochastic analysis. The Bayesian ensemble learning model was employed to enhance prediction accuracy, with the Shapley additive explanations method employed to elucidate the contribution of each input variable. The proposed method displayed satisfactory performance, achieving an area under the curve of 0.9973, accuracy of 0.9727, and F1-score of 0.9729, surpassing the compared ML methods. Its robustness and generalization capabilities were confirmed through evaluations on diverse datasets and random seeds. Furthermore, a user-friendly graphical user interface was created for fast estimation of slope stability (FESS) using the trained prediction model. The performance of FESS was validated on a series of examples including the Australian Association for Computer-Aided Design referenced slope example EX1 and 77 in situ cases. This tool offers practitioners a high-performance solution, significantly reducing the effort required for slope stability assessments.

Keywords: Fast estimation of slope stability (FESS); Physics-informed machine learning (ML); Nonintrusive stochastic analysis; Ensemble learning; Bayesian optimization; Shapley additive explanations (SHAP) (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-024-06917-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:121:y:2025:i:3:d:10.1007_s11069-024-06917-2

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-024-06917-2

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().