Scouring around bridge piers is a highly nonlinear process making its prediction by deterministic and stochastic models challenging. This study explores the application of inferential models for predictions of bed elevations around bridge piers. The objective is to get a generalized machine learning model with an interpretable structure. The historical data comprise a detailed record of streamflow and bed elevations that were captured by sensors installed at the 5th Street Bridge piers over Ocmulgee River at Macon, GA. We investigate the accuracy and efficiency of various tree-based machine learning algorithms, including a single tree as well as homogeneous ensemble models for simultaneous predictions of bed elevation at multiple sensors installed at piers. The ensemble models were based on bagging and boosting techniques. Special attention is given to balancing between overfitting and underfitting without compromise on the model's robustness. Observation of the performance metrics showed that tree-based models have excellent predictive capacity. It was observed that boosting models, including a gradient based regression model, and adaptive boosting outperformed the bagging model. Among all the models investigated in this study, the adaptive boosting method was observed to be most generalizable. The performance of developed models shows the potential of tree-based ensemble models in providing rapid and robust predictions for complex nonlinear fluid flows.
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August 2022
Research Article|
August 05 2022
Tree-based machine learning models for prediction of bed elevation around bridge piers
Khawar Rehman
;
Khawar Rehman
a)
(Formal analysis, Funding acquisition, Methodology, Writing – original draft, Writing – review & editing)
1
Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology
, Topi, Swabi 23460, Pakistan
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Yung-Chieh Wang
;
Yung-Chieh Wang
b)
(Formal analysis, Writing – original draft)
2
Department of Soil and Water Conservation, National Chung Hsing University
, Taichung City, Taiwan
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Muhammad Waseem;
Muhammad Waseem
c)
(Conceptualization, Formal analysis, Writing – original draft, Writing – review & editing)
1
Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology
, Topi, Swabi 23460, Pakistan
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Seung Ho Hong
Seung Ho Hong
d)
(Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing)
3
Department of Civil and Environmental Engineering, Hanyang University
, Ansan 15588, South Korea
d)Author to whom correspondence should be addressed: [email protected]. Tel.: 82-031-400-5143.
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d)Author to whom correspondence should be addressed: [email protected]. Tel.: 82-031-400-5143.
Physics of Fluids 34, 085105 (2022)
Article history
Received:
May 08 2022
Accepted:
July 12 2022
Citation
Khawar Rehman, Yung-Chieh Wang, Muhammad Waseem, Seung Ho Hong; Tree-based machine learning models for prediction of bed elevation around bridge piers. Physics of Fluids 1 August 2022; 34 (8): 085105. https://doi.org/10.1063/5.0098394
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