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Approximation of the Discharge Coefficient of Radial Gates Using Metaheuristic Regression Approaches

Author

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  • Parveen Sihag

    (Department of Civil Engineering, Chandigarh University, Punjab 43521-15862, India)

  • Meysam Nouri

    (Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia 57561-51818, Iran
    Department of Civil Engineering, Saeb University, Abhar 45717-74783, Iran)

  • Hedieh Ahmadpari

    (Department of Irrigation and Reclamation Engineering, College of Aburaihan, University of Tehran, Tehran 57561-51818, Iran)

  • Amin Seyedzadeh

    (Department of Water Engineering, Faculty of Agriculture, Fasa University, Fasa 57561-51818, Iran)

  • Ozgur Kisi

    (Department of Civil Engineering, Technical University of Lübeck, 23562 Lübeck, Germany
    Department of Civil Engineering, Ilia State University, 0162 Tbilisi, Georgia)

Abstract

Radial gates are widely used for agricultural water management, flood controlling, etc. The existence of methods for the calculation of the discharge coefficient ( C d ) of such gates are complex and they are based on some assumptions. The development of new usable and simple models is needed for the prediction of C d . This study investigates the viability of a metaheuristic regression method, the Gaussian Process (GP), for the determination of the discharge coefficient of radial gates. For this purpose, a total of 2536 experimental data were compiled that cover a wide range of all the effective parameters. The results of GP were compared with the Group Method of Data Handling (GMDH), Multivariate Adaptive Regression Splines (MARS), and linear and nonlinear regression models for predicting C d of radial gates in both free-flow and submerged-flow conditions. The results revealed that the radial basis function-based GP model performed the best in free-flow condition with a Correlation Coefficient (CC) of 0.9413 and Root Mean Square Error (RMSE) of 0.0190 while the best accuracy was obtained from the Pearson VII kernel function-based GP model for the submerged flow condition with a CC of 0.9961 and RMSE of 0.0132.

Suggested Citation

  • Parveen Sihag & Meysam Nouri & Hedieh Ahmadpari & Amin Seyedzadeh & Ozgur Kisi, 2022. "Approximation of the Discharge Coefficient of Radial Gates Using Metaheuristic Regression Approaches," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15145-:d:973610
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    References listed on IDEAS

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    1. Elbeltagi, Ahmed & Azad, Nasrin & Arshad, Arfan & Mohammed, Safwan & Mokhtar, Ali & Pande, Chaitanya & Etedali, Hadi Ramezani & Bhat, Shakeel Ahmad & Islam, Abu Reza Md. Towfiqul & Deng, Jinsong, 2021. "Applications of Gaussian process regression for predicting blue water footprint: Case study in Ad Daqahliyah, Egypt," Agricultural Water Management, Elsevier, vol. 255(C).
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