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Granular Computing for Prediction of Scour Below Spillways

Author

Listed:
  • Roohollah Noori

    (University of Tehran)

  • Hossien Sheikhian

    (University of Tehran)

  • Farhad Hooshyaripor

    (Islamic Azad University)

  • Ali Naghikhani

    (University of Tehran)

  • Jan Franklin Adamowski

    (McGill University)

  • Behzad Ghiasi

    (University of Tehran)

Abstract

Effective estimation of scour parameters downstream ski-jump buckets is very important for risk management plan. This paper presents a new method for prediction of the depth, length, and width of the scour hole downstream ski-jump buckets based on granular computing (GrC) technique. This method employs various independent hydraulic, morphologic, and geotechnical factors to predict dependent scour parameters. Evaluation of the results indicated that the dependent scour parameters are affected more by the discharge, falling height, and mean sediment size and less by the lip angle of the bucket. Analyses of the obtained results demonstrated the high accuracy of the GrC, as the predicted values were in good agreement with the observations. Furthermore, statistical equations were derived based on the multiple linear regressions (MLR) to model the relationship between the scour parameters. Despite our expectations, the results of MLR, as a simple model, were excellent as compared to GrC. MLR results were also superior to those of well-known empirical equations presented to date. The GrC gives the best performance for the prediction of scour parameters; however, MLR model is also suggested for any real cases because it can be more applicable by practical engineers than GrC as a black box model.

Suggested Citation

  • Roohollah Noori & Hossien Sheikhian & Farhad Hooshyaripor & Ali Naghikhani & Jan Franklin Adamowski & Behzad Ghiasi, 2017. "Granular Computing for Prediction of Scour Below Spillways," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 313-326, January.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:1:d:10.1007_s11269-016-1526-0
    DOI: 10.1007/s11269-016-1526-0
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    References listed on IDEAS

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