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

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  • Vasileios Kitsikoudis
  • Epaminondas Sidiropoulos
  • Lazaros Iliadis
  • Vlassios Hrissanthou

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. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Vasileios Kitsikoudis & Epaminondas Sidiropoulos & Lazaros Iliadis & Vlassios Hrissanthou, 2015. "A Machine Learning Approach for the Mean Flow Velocity Prediction in Alluvial Channels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(12), pages 4379-4395, September.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:12:p:4379-4395
    DOI: 10.1007/s11269-015-1065-0
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    References listed on IDEAS

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    1. H. Azamathulla & Robert Jarrett, 2013. "Use of Gene-Expression Programming to Estimate Manning’s Roughness Coefficient for High Gradient Streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 715-729, February.
    2. Hazi Azamathulla & Aminuddin Ghani & Cheng Leow & Chun Chang & Nor Zakaria, 2011. "Gene-Expression Programming for the Development of a Stage-Discharge Curve of the Pahang River," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(11), pages 2901-2916, September.
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    Cited by:

    1. Majid Niazkar & Nasser Talebbeydokhti & Seied Hosein Afzali, 2019. "Novel Grain and Form Roughness Estimator Scheme Incorporating Artificial Intelligence Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 757-773, January.
    2. Gokmen Tayfur, 2017. "Modern Optimization Methods in Water Resources Planning, Engineering and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3205-3233, August.

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