An expert system for predicting Manning's roughness coefficient in open channels by using gene expression programming
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- @Article{journals/nca/AzamathullaAG13,
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title = "An expert system for predicting Manning's roughness
coefficient in open channels by using gene expression
programming",
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author = "H. Md. Azamathulla and Zulfequar Ahmad and
Aminuddin {Ab. Ghani}",
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journal = "Neural Computing and Applications",
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year = "2013",
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number = "5",
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volume = "23",
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keywords = "genetic algorithms, genetic programming, gene
expression programming, GEP",
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bibdate = "2013-11-19",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/nca/nca23.html#AzamathullaAG13",
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pages = "1343--1349",
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URL = "http://dx.doi.org/10.1007/s00521-012-1078-z",
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size = "7 pages",
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abstract = "Manning's roughness coefficient (n) has been widely
used in the estimation of flood discharges or depths of
flow in natural channels. Accurate estimation of
Manning's roughness coefficient is essential for the
computation of flow rate, velocity. Conventional
formulae that are greatly based on empirical methods
lack in providing high accuracy for the prediction of
Manning's roughness coefficient. Consequently, new and
accurate techniques are still highly demanded. In this
study, gene expression programming (GEP) is used to
estimate the Manning roughness coefficient. The
estimated value of the roughness coefficient is used in
Mannings equation to compute the flow parameters in
open-channel flows in order to carry out a comparison
between the proposed GEP-based approach and the
conventional ones. Results show that computed discharge
using estimated value of roughness coefficient by GEP
is in good agreement (10percent) with the experimental
results compared to the conventional formulae
(R-squared = 0.97 and RMSE = 0.0034 for the training
data and Rsquared = 0.94 and RMSE = 0.086 for the
testing data).",
- }
Genetic Programming entries for
Hazi Mohammad Azamathulla
Z Ahmad
Aminuddin Ab Ghani
Citations