Prediction of Flow Resistance in an Open Channel over Movable Beds Using Artificial Neural Network
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VIEW THE REPLYPublication: Journal of Hydrologic Engineering
Volume 26, Issue 5
Abstract
Estimating flow resistance is essential for the hydraulic analysis of a river and the evaluation of conveyance in a specific flow condition. Under bed-load transport conditions, the resistance to the flow in an open channel is different from fixed-bed condition and requires a distinct method for its evaluation. The geometric and hydraulic parameters influence flow resistance characteristics in the mobile bed load. In the present study, a wide range of experimental flume data sets are investigated to derive the dependency of the dimensionless parameters on the flow resistance under mobile bed-load conditions. The five most important dimensionless parameters, such as relative submergence depth, bed slope, aspect ratio, Reynolds number, and Froude number, are suggested because they show a unique relationship to the dependent parameter. An artificial neural network (ANN) model to predict the flow resistance is proposed by considering these independent parameters as the input parameters. To verify the strength of the model, the performances of previous researchers’ models were also evaluated and compared with the present work by considering a wide range of data sets. It is found that the previous models can be used for a specific range of data sets only, whereas the proposed ANN-based model is capable of performing well for a wide range of geometric and hydraulic conditions of a channel.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article. For the data sets used in this article, please refer to Appendixes I and II.
Acknowledgments
The authors wish to express sincere thanks to the anonymous editors and reviewers for their time in effort in reviewing and offering constructive suggestions that improved the manuscript. We are thankful to Dr. Kamalini Devi, Associate Professor, Vidya Jyothi Institute of Technology (VJIT) Hyderabad, and Dr. Shreedevi Moharana, NPDF, Indian Institute of Technology (IIT) Hyderabad, for their expertise and suggestions in revising the manuscript. The authors would like to sincerely thank all the previous researchers for the valuable experimental data sets. All the researchers listed in the references are also sincerely acknowledged.
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Journal of Hydrologic Engineering
Volume 26 • Issue 5 • May 2021
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© 2021 American Society of Civil Engineers.
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Received: May 12, 2020
Accepted: Jan 4, 2021
Published online: Mar 13, 2021
Published in print: May 1, 2021
Discussion open until: Aug 13, 2021
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