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Evaluation of Flow Resistance using Multi-Gene Genetic Programming for Bed-load Transport in Gravel-bed Channels

Author

Listed:
  • Satish Kumar

    (National Institute of Technology Rourkela)

  • Arpan Pradhan

    (CHRIST (Deemed to be University))

  • Jnana Ranjan Khuntia

    (Chaitanya Bharathi Institute of Technology (A))

  • Kishanjit Kumar Khatua

    (National Institute of Technology Rourkela)

Abstract

Evaluation of flow resistance is necessary for the computation of conveyance capacity in open channels. The significance of the friction factor in channels with bedload conditions is paramount. The response of flow resistance in gravel-bed channels in bedload transport conditions is distinct from that of a fixed bed. The paper studies the different empirical approaches in the literature to determine the friction factor under bedload transport conditions and proposes an expression by genetic programming for the same. Various hydraulic and geometric parameters affect flow resistance in the bedload transport condition. The present study includes bed slope, relative submergence depth, aspect ratio, Reynolds number, and Froude number as influencing factors for such flow conditions. A wide range of experimental datasets is employed to determine the effect of these influencing parameters and develop a customised single expression for the friction factor. The experimental data set has also been moderated for sidewall corrections. The predictability of the proposed model is compared to various empirical equations from the literature. Unlike the existing models, the proposed model provides a more extensive expression for effectively predicting the friction factor for a wide range of datasets. The conveyance capacity of a river is validated from the estimated value of friction factor, as compared to other standard models. The developed Multi-Gene Genetic Programming (MGGP) model reasonably predicts discharge in the rivers, signifying that the model can competently be applied to field study within the specified range of parameters.

Suggested Citation

  • Satish Kumar & Arpan Pradhan & Jnana Ranjan Khuntia & Kishanjit Kumar Khatua, 2023. "Evaluation of Flow Resistance using Multi-Gene Genetic Programming for Bed-load Transport in Gravel-bed Channels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 2945-2967, June.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:8:d:10.1007_s11269-022-03409-5
    DOI: 10.1007/s11269-022-03409-5
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    References listed on IDEAS

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    1. Isa Ebtehaj & Hossein Bonakdari, 2014. "Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4765-4779, October.
    2. Aly K. Salem & Yehya E. Imam & Ashraf H. Ghanem & Abdallah S. Bazaraa, 2022. "Genetic Algorithm Based Model for Optimal Selection of Open Channel Design Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 5867-5896, December.
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