IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i22p15145-d973610.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/22/15145/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/22/15145/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vishwakarma, Dinesh Kumar & Pandey, Kusum & Kaur, Arshdeep & Kushwaha, N.L. & Kumar, Rohitashw & Ali, Rawshan & Elbeltagi, Ahmed & Kuriqi, Alban, 2022. "Methods to estimate evapotranspiration in humid and subtropical climate conditions," Agricultural Water Management, Elsevier, vol. 261(C).
    2. Mahmood Ahmad & Suraparb Keawsawasvong & Mohd Rasdan Bin Ibrahim & Muhammad Waseem & Kazem Reza Kashyzadeh & Mohanad Muayad Sabri Sabri, 2022. "Novel Approach to Predicting Soil Permeability Coefficient Using Gaussian Process Regression," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
    3. Gerkani Nezhad Moshizi, Zahra & Bazrafshan, Ommolbanin & Ramezani Etedali, Hadi & Esmaeilpour, Yahya & Collins, Brain, 2023. "Application of inclusive multiple model for the prediction of saffron water footprint," Agricultural Water Management, Elsevier, vol. 277(C).
    4. Abhinav Kumar Singh & Pankaj Kumar & Rawshan Ali & Nadhir Al-Ansari & Dinesh Kumar Vishwakarma & Kuldeep Singh Kushwaha & Kanhu Charan Panda & Atish Sagar & Ehsan Mirzania & Ahmed Elbeltagi & Alban Ku, 2022. "An Integrated Statistical-Machine Learning Approach for Runoff Prediction," Sustainability, MDPI, vol. 14(13), pages 1-30, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15145-:d:973610. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.