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Use of gene-expression programming to estimate Manning's roughness coefficient for a low flow stream
Chaplot, B. Peters, M. Birbal, P. Pu, Jaan H. Shafie, A.
Chaplot, B.
Peters, M.
Birbal, P.
Pu, Jaan H.
Shafie, A.
Publication Date
2021-12
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© 2021 Chaplot B. and al.; This is an Open Access article distributed under the terms of the Creative Commons Attribution
License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any
medium, provided the original work is properly cited
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openAccess
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2021-12-10
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Abstract
Manning’s roughness coefficient (n) has been widely used to estimate flood discharges and flow depths in natural channels. Therefore, although extensive guidelines are available, the selection of the appropriate n value is of great importance to hydraulic engineers and hydrologists. Generally, the largest source of error in post-flood estimates is caused by the estimation of n values, particularly when there has been minimal field verification of flow resistance. This emphasizes the need to improve methods for evaluating the roughness coefficients. Trinidad and Tobago currently does not have any set method or standardised procedure that they use to determine the n value. Therefore, the objective of this study was to develop a soft computing model in the calculation of the roughness coefficient values using low flow discharge measurements for a stream. This study presents Gene-Expression Programming (GEP), as an improved approach to compute Manning’s Roughness Coefficient. The GEP model was found to be accurate, producing a coefficient of determination (R2) of 0.94 and Root Mean Square Error (RSME) of 0.0024.
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Citation
Chaplot B, Peters M, Birbal P et al (2021) Use of gene-expression programming to estimate Manning's roughness coefficient for a low flow stream. Larhyss Journal. 48: 135-150.
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Article