- Title
- Enhancing earth dam slope stability prediction with integrated AI and statistical models
- Creator
- Baghbani, Abolfazl; Faradonbeh, Roohollah; Lu, Yi; Soltani, Amin; Kiany, Katayoon; Baghbani, Hasan; Abuel-Naga, Hossam; Samui, Pijush
- Date
- 2024
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/204822
- Identifier
- vital:20087
- Identifier
-
https://doi.org/10.1016/j.asoc.2024.111999
- Identifier
- ISSN:1568-4946 (ISSN)
- Abstract
- This study introduces an innovative approach integrating artificial intelligence (AI) and statistical modelling techniques to enhance the prediction of earth dam slope stability. Utilizing advanced methodologies, including Classification and Regression Tree (CART), Classification and Regression Random Forests (CRRF), and Multiple Linear Regression (MLR), this research provides a comprehensive, data-driven analysis for slope stability prediction. The integration of AI with traditional statistical models significantly improves the predictive accuracy over conventional methods. The findings reveal the model's capability in terms of reliability and precision in predicting slope stability, demonstrating its potential as a powerful tool in geo-engineering. Additionally, this work highlights the effective application of AI in complex geo-environmental systems analysis, opening new avenues for research and practice in the field. © 2024 Elsevier B.V.
- Publisher
- Elsevier Ltd
- Relation
- Applied Soft Computing Vol. 164, no. (2024), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2024 Elsevier B.V.
- Subject
- 4602 Artificial intelligence; 4903 Numerical and computational mathematics; Artificial intelligence (AI); Classification and Regression Random Forests (CRRF); Classification and Regression Tree (CART); Factor of safety; Sensitivity analysis; Slope stability
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