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Distribution and Prediction of Boundary Shear in Diverging Compound Channels

Author

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  • B. Sree Sai Prasad

    (National Institute of Technology Rourkela)

  • Anurag Sharma

    (National Institute of Technology Rourkela)

  • Kishanjit Kumar Khatua

    (National Institute of Technology Rourkela)

Abstract

Measurement of bed shear stress is always a challenging task for engineers. In river engineering, bed shear is a fundamental variable and is important in estimating flow resistance and sediment transport. In this study, experiments are carried out in diverging compound channel with smooth bed (perspex sheet) and rough bed (Gravel) conditions to determine the effect of roughness. The shear velocity is estimated from universal logarithmic law. The effect of geometry and roughness on Von-Karman constant, eddy viscosity coefficient, friction factor is studied. The mass conservation and momentum conservation equations are used to derive apparent shear forces at interface of main channel and floodplain. A genetic algorithm model is developed to predict percentage of shear force (%Sfp) carried by sub-sections. To perform better with less and unseen data K-Fold cross-validation technique is used. The model is compared with available models in literature and it is observed that developed model gave better predictions with low MAPE.

Suggested Citation

  • B. Sree Sai Prasad & Anurag Sharma & Kishanjit Kumar Khatua, 2022. "Distribution and Prediction of Boundary Shear in Diverging Compound Channels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 4965-4979, October.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:13:d:10.1007_s11269-022-03286-y
    DOI: 10.1007/s11269-022-03286-y
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    References listed on IDEAS

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    1. Abinash Mohanta & K. C. Patra & Arpan Pradhan, 2020. "Enhanced Channel Division Method for Estimation of Discharge in Meandering Compound Channel," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1047-1073, February.
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    Cited by:

    1. Seyed Morteza Seyedian & Ozgur Kisi & Abbas Parsaie & Mojtaba Kashani, 2024. "Improving the Reliability of Compound Channel Discharge Prediction Using Machine Learning Techniques and Resampling Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4685-4709, September.

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