Machine Learning Approach to Modeling Sediment Transport

Inaccuracies of sediment transport models largely originate from our limitation to describe the process in precise mathematical terms. Machine learning (ML) is an alternative approach to reduce the inaccuracies of sedimentation models. It utilizes available domain knowledge for selecting the input a...

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Bibliographic Details
Published in:Journal of hydraulic engineering (New York, N.Y.) N.Y.), 2007-04, Vol.133 (4), p.440-450
Main Authors: Bhattacharya, B, Price, R. K, Solomatine, D. P
Format: Article
Language:English
Subjects:
Applied sciences
Buildings. Public works
Computation methods. Tables. Charts
Exact sciences and technology
Hydraulic constructions
Structural analysis. Stresses
TECHNICAL PAPERS
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Summary:Inaccuracies of sediment transport models largely originate from our limitation to describe the process in precise mathematical terms. Machine learning (ML) is an alternative approach to reduce the inaccuracies of sedimentation models. It utilizes available domain knowledge for selecting the input and output variables for the ML models and uses modern regression techniques to fit the measured data. Two ML methods, artificial neural networks and model trees, are adopted to model bed-load and total-load transport using the measured data. The bed-load transport models are compared with the models due to Bagnold, Einstein, Parker et al., and van Rijn. The total-load transport models are compared with the models due to Ackers and White, Bagnold, Engelund and Hansen, and van Rijn. With the chosen data sets on bed-load and total-load transport the ML models provided better accuracy than the existing ones.
ISSN:0733-9429
1943-7900
DOI:10.1061/(ASCE)0733-9429(2007)133:4(440)