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A Machine Learning Approach for the Mean Flow Velocity Prediction in Alluvial Channels

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Abstract

In natural alluvial channels, the determination of the flow resistance constitutes a problem with additional complexity compared to rigid bed channels, due to the bed morphology transformations and the alterations of the flow properties caused by sediment transport. While there have been steps towards understanding the processes that contribute to flow resistance in an alluvial channel, a robust quantitative model with wide applicability remains elusive. Machine learning offers the ability to exploit available data and generate equations that accurately describe the problem by taking implicitly into account the contributing mechanisms that are difficult to be modeled. In this paper, four machine learning techniques are employed for the mean flow velocity prediction, separately for sand-bed and gravel-bed rivers, namely artificial neural networks, adaptive-network-based fuzzy inference system, symbolic regression based on genetic programming, and support vector regression. The derived models are robust and their results are superior to those of some widely used flow resistance formulae, which compute the mean flow velocity from similar independent hydraulic variables.

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Acknowledgments

The authors are grateful to an Associate Editor and two anonymous Reviewers for their constructive and insightful comments and suggestions, which improved the presentation of this paper.

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Authors and Affiliations

  1. Department of Civil Engineering, Democritus University of Thrace, Vasil. Sophias 12, Xanthi, 67100, Greece

    Vasileios Kitsikoudis & Vlassios Hrissanthou

  2. Department of Rural and Surveying Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece

    Epaminondas Sidiropoulos

  3. Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Pandazidou 193, N. Orestiada, 68200, Greece

    Lazaros Iliadis

Authors
  1. Vasileios Kitsikoudis
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  2. Epaminondas Sidiropoulos
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  3. Lazaros Iliadis
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  4. Vlassios Hrissanthou
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Correspondence to Vasileios Kitsikoudis.

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Kitsikoudis, V., Sidiropoulos, E., Iliadis, L. et al. A Machine Learning Approach for the Mean Flow Velocity Prediction in Alluvial Channels. Water Resour Manage 29, 4379–4395 (2015). https://doi.org/10.1007/s11269-015-1065-0

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