Prediction of flow discharge in compound open channels using adaptive neuro fuzzy inference system method

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@Article{Parsaie:2017:FMI,
  author =       "Abbas Parsaie and Hojjatallah Yonesi and 
                 Shadi Najafian",
  title =        "Prediction of flow discharge in compound open channels
                 using adaptive neuro fuzzy inference system method",
  journal =      "Flow Measurement and Instrumentation",
  volume =       "54",
  pages =        "288--297",
  year =         "2017",
  ISSN =         "0955-5986",
  DOI =          "doi:10.1016/j.flowmeasinst.2016.08.013",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0955598616301157",
  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.",
  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