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Genetic Programming: A Complementary Approach for Discharge Modelling in Smooth and Rough Compound Channels

Genetic Programming: A Complementary Approach for Discharge Modelling in Smooth and Rough... Use of genetic programming (GP) in the field of river engineering is rare. During flood when the water overflows beyond its main course known as floodplain encounters various obstacles through rough materials and vegetation. Again the flow behaviour becomes more complex in a compound channel section due to shear at different regions. Discharge results from the experimental channels for varying roughness surfaces, along with data from a compound river section, are used in the GP. Model equations are derived for prediction of discharge in the compound channel using five hydraulic parameters. Derived models are tested and compared with other soft computing techniques. Few performance parameters are used to evaluate all the approaches taken for analysis. From the sensitivity analysis, the effects of parameters responsible for the flow behaviour are inferred. GP is found to give the most potential results with the highest level of accuracy. This work aims to benefit the researchers studying machine learning approaches for application in stream flow analysis. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of The Institution of Engineers (India): Series A Springer Journals

Genetic Programming: A Complementary Approach for Discharge Modelling in Smooth and Rough Compound Channels

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Publisher
Springer Journals
Copyright
Copyright © 2019 by The Institution of Engineers (India)
Subject
Engineering; Civil Engineering
ISSN
2250-2149
eISSN
2250-2157
DOI
10.1007/s40030-019-00367-x
Publisher site
See Article on Publisher Site

Abstract

Use of genetic programming (GP) in the field of river engineering is rare. During flood when the water overflows beyond its main course known as floodplain encounters various obstacles through rough materials and vegetation. Again the flow behaviour becomes more complex in a compound channel section due to shear at different regions. Discharge results from the experimental channels for varying roughness surfaces, along with data from a compound river section, are used in the GP. Model equations are derived for prediction of discharge in the compound channel using five hydraulic parameters. Derived models are tested and compared with other soft computing techniques. Few performance parameters are used to evaluate all the approaches taken for analysis. From the sensitivity analysis, the effects of parameters responsible for the flow behaviour are inferred. GP is found to give the most potential results with the highest level of accuracy. This work aims to benefit the researchers studying machine learning approaches for application in stream flow analysis.

Journal

Journal of The Institution of Engineers (India): Series ASpringer Journals

Published: Feb 7, 2019

References