Discussion of “Prediction of Flow Resistance in an Open Channel over Movable Beds Using Artificial Neural Network” by Satish Kumar, Jnana Ranjan Khuntia, and Kishanjit Kumar Khatua
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VIEW THE ORIGINAL ARTICLEAuthors: Isa Ebtehaj https://orcid.org/0000-0002-6906-629X [email protected] and Hossein Bonakdari https://orcid.org/0000-0001-6169-3654 [email protected]Author Affiliations
Publication: Journal of Hydrologic Engineering
Volume 28, Issue 2
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Data Availability Statement
No data, models, or codes were generated or used during the study.
Acknowledgments
The authors acknowledge the financial support provided by the Natural Science and Engineering Research Council of Canada (NSERC) through Discovery Grant RGPIN-2020-04583.
References
Bonakdari, H., H. Moeeni, I. Ebtehaj, M. Zeynoddin, A. Mahoammadian, and B. Gharabaghi. 2019. “New insights into soil temperature time series modeling: Linear or nonlinear?” Theor. Appl. Climatol. 135 (3): 1157–1177. https://doi.org/10.1007/s00704-018-2436-2.
Ebtehaj, I., H. Bonakdari, and A. H. Zaji. 2018. “A new hybrid decision tree method based on two artificial neural networks for predicting sediment transport in clean pipes.” Alexandria Eng. J. 57 (3): 1783–1795. https://doi.org/10.1016/j.aej.2017.05.021.
Meyer-Peter, E., and R. Müller. 1948. “Formulas for bedload transport.” In Proc., IAHSR 2nd Meeting. Stockholm, Sweden: International Association for Hydro-Environment Engineering and Research.
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© 2022 American Society of Civil Engineers.
History
Received: Jul 20, 2021
Accepted: Feb 8, 2022
Published online: Dec 2, 2022
Published in print: Feb 1, 2023
Discussion open until: May 2, 2023
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Ph.D. Candidate, Dept. of Soils and Agri-Food Engineering, Université Laval, Québec, Canada G1V0A6. ORCID: https://orcid.org/0000-0002-6906-629X. Email: [email protected]
Assistant Professor, Dept. of Soils and Agri-Food Engineering, Université Laval, Québec, Canada G1V0A6; Associate Professor, Dept. of Civil Engineering, Univ. of Ottawa, 161 Louis Pasteur Dr., Ottwa, Canada K1N6N5 (corresponding author). ORCID: https://orcid.org/0000-0001-6169-3654. Email: [email protected]; [email protected]
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