Machine Learning Approach to Modeling Sediment Transport
Publication: Journal of Hydraulic Engineering
Volume 133, Issue 4
Abstract
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.
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Acknowledgments
Part of this work was performed in the framework of the Delft Cluster Research Program supported by the Dutch government. The writers would also like to thank Professor M. Church (University of British Columbia, Canada) for providing the data on bed-load transport. The writers are also grateful to Professor L. C. van Rijn (Delft Hydraulics and Utrecht University, The Netherlands) for providing useful advice on using Brownlie’s compilation of sediment transport data.
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Information & Authors
Information
Published In
Journal of Hydraulic Engineering
Volume 133 • Issue 4 • April 2007
Pages: 440 - 450
Copyright
© 2007 ASCE.
History
Received: Jul 13, 2004
Accepted: Aug 30, 2006
Published online: Apr 1, 2007
Published in print: Apr 2007
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