On the Prediction of Discharge Coefficient for Sluice Gates under Submerged Flow Conditions using Soft Computing Techniques

Document Type : Civil Article

Authors

1 Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

2 Associate Professor in Civil Eng. Hydraulics & River Eng. Mechanics, Department of Civil Engineering, Faculty of Eng., Urmia University.

3 MSc Student, Department of Civil Eng., Faculty of Eng., Urmia University, Urmia, Iran.

4 Assistant Professor, Department of Civil Eng., Faculty of Eng., Bursa Technical University, Bursa, Turkey.

Abstract

Prediction of flow discharge coefficient, Cd, for a sluice gate under free and submerged flow conditions is one of the essential issues in hydraulics. In recent years, various semi-empirical equations have been developed in order to predict Cd for a sluice gate that application of those formulas under submerged flow conditions suffered from large errors. The aim of the present research is to use Gaussian Process Regression (GPR) and Support Vector Machine (SVM) used in soft computing techniques, so that estimating Cd in submerged flow conditions and comparing the results with quasi-experimental methods are of interest, herein. For this purpose, an experimental dataset comprised of 122 data points were used to feed the methods utilized. Different combinations of dimensionless parameters were then prepared and the performance of the afore mentioned methods were assessed. The results showed that SVM with input parameters of 𝑦𝑡⁄𝑤, 𝑦0⁄𝑤, 1/𝐹𝑟2 and S by the values of Root Mean Square Error (RMSE=0.017), correlation coefficient (R=0.97) and Nash-Sutcliffe Equivalent (NSE=0.95) had a better performance than GPR and other semi-empirical approaches, indeed.

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References

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Journal of Modeling in Engineering
Volume 20, Issue 71
November 2022
Pages 1-12

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History

  • Receive Date: 14 May 2021
  • Revise Date: 26 July 2021
  • Accept Date: 24 January 2022

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