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,

• title = "An expert system for predicting Manning's roughness coefficient in open channels by using gene expression programming",
• author = "H. Md. Azamathulla and Zulfequar Ahmad and Aminuddin {Ab. Ghani}",
• journal = "Neural Computing and Applications",
• year = "2013",
• number = "5",
• volume = "23",
• keywords = "genetic algorithms, genetic programming, gene expression programming, GEP",
• bibdate = "2013-11-19",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/nca/nca23.html#AzamathullaAG13",
• pages = "1343--1349",
• URL = "http://dx.doi.org/10.1007/s00521-012-1078-z",
• size = "7 pages",
• 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