Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@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