Prediction of flow discharge in compound open channels using adaptive neuro fuzzy inference system method
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- @Article{Parsaie:2017:FMI,
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author = "Abbas Parsaie and Hojjatallah Yonesi and
Shadi Najafian",
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title = "Prediction of flow discharge in compound open channels
using adaptive neuro fuzzy inference system method",
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journal = "Flow Measurement and Instrumentation",
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volume = "54",
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pages = "288--297",
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year = "2017",
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ISSN = "0955-5986",
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DOI = "doi:10.1016/j.flowmeasinst.2016.08.013",
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URL = "http://www.sciencedirect.com/science/article/pii/S0955598616301157",
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abstract = "Discharge estimation in rivers is the most important
parameter in flood management. Predicting discharge in
the compound open channel by analytical approach leads
to solving a system of complex nonlinear equations. In
many complex mathematical problems that lead to solving
complex problems, an artificial intelligence model
could be used. In this study, the adaptive neuro fuzzy
inference system (ANFIS) is used for modeling and
predicting of flow discharge in the compound open
channel. Comparison of results showed that the divided
channel method with horizontal division lines with the
Coefficient of determination (0.76) and root mean
square error (0.162) is accurate among the analytical
approaches. The ANFIS model with the coefficient of
determination (0.98) and root mean square error (0.029)
for the testing stage has suitable performance for
predicting the discharge of flow in the compound open
channel. During the development of the ANFIS model,
found that the relative depth, ratio of hydraulics
radius and ratio of the area are the most influencing
parameters in discharge prediction by the ANFIS
model.",
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keywords = "genetic algorithms, genetic programming, Soft
computing, Discharge prediction, Flood engineering,
ANFIS, River hydraulic",
- }
Genetic Programming entries for
Abbas Parsaie
Hojjatallah Yonesi
Shadi Najafian
Citations