IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v29y2015i12p4379-4395.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-015-1065-0
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11269-015-1065-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Anas Mahmood Al-Juboori & Aytac Guven, 2016. "Hydropower Plant Site Assessment by Integrated Hydrological Modeling, Gene Expression Programming and Visual Basic Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(7), pages 2517-2530, May.
    3. 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.
    4. Vesna Đukić & Zoran Radić, 2016. "Sensitivity Analysis of a Physically Based Distributed Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1669-1684, March.
    5. Vesna Đukić & Zoran Radić, 2016. "Sensitivity Analysis of a Physically Based Distributed Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1669-1684, March.
    6. Kazem Shahverdi & Hossein Talebmorad, 2023. "Automating HEC-RAS and Linking with Particle Swarm Optimizer to Calibrate Manning’s Roughness Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 975-993, January.
    7. E. Fallah-Mehdipour & O. Bozorg Haddad & H. Orouji & M. Mariño, 2013. "Application of Genetic Programming in Stage Hydrograph Routing of Open Channels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3261-3272, July.
    8. Seydou Traore & Aytac Guven, 2012. "Regional-Specific Numerical Models of Evapotranspiration Using Gene-Expression Programming Interface in Sahel," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4367-4380, December.
    9. Vasileios Kitsikoudis & Epaminondas Sidiropoulos & Vlassios Hrissanthou, 2014. "Machine Learning Utilization for Bed Load Transport in Gravel-Bed Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3727-3743, September.
    10. Arash Adib & Arash Zaerpour & Ozgur Kisi & Morteza Lotfirad, 2021. "A Rigorous Wavelet-Packet Transform to Retrieve Snow Depth from SSMIS Data and Evaluation of its Reliability by Uncertainty Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2723-2740, July.
    11. Yassin, Mohamed A. & Alazba, A.A. & Mattar, Mohamed A., 2016. "Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate," Agricultural Water Management, Elsevier, vol. 163(C), pages 110-124.
    12. Tabasum Rasool & A. Q. Dar & M. A. Wani, 2021. "Development of a Predictive Equation for Modelling the Infiltration Process Using Gene Expression Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1871-1888, April.
    13. E. Fallah-Mehdipour & O. Bozorg Haddad & M. Mariño, 2012. "Real-Time Operation of Reservoir System by Genetic Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(14), pages 4091-4103, November.
    14. Mohammad Bahrami Yarahmadi & Abbas Parsaie & Mahmood Shafai-Bejestan & Mostafa Heydari & Marzieh Badzanchin, 2023. "Estimation of Manning Roughness Coefficient in Alluvial Rivers with Bed Forms Using Soft Computing Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3563-3584, July.
    15. Neslihan Seckin & Aytac Guven, 2012. "Estimation of Peak Flood Discharges at Ungauged Sites Across Turkey," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(9), pages 2569-2581, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:29:y:2015:i:12:p:4379-4395. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.