Flow discharge prediction in compound channels using linear genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "H. Md. Azamathulla and A. Zahiri",
  title =        "Flow discharge prediction in compound channels using
                 linear genetic programming",
  journal =      "Journal of Hydrology",
  volume =       "454-455",
  pages =        "203--207",
  year =         "2012",
  month =        "6 " # aug,
  ISSN =         "0022-1694",
  DOI =          "doi:10.1016/j.jhydrol.2012.05.065",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0022169412004684",
  keywords =     "genetic algorithms, genetic programming,
                 Stage-discharge curve, Flooded rivers, Floodplains",
  abstract =     "Flow discharge determination in rivers is one of the
                 key elements in mathematical modelling in the design of
                 river engineering projects. Because of the inundation
                 of floodplains and sudden changes in river geometry,
                 flow resistance equations are not applicable for
                 compound channels. Therefore, many approaches have been
                 developed for modification of flow discharge
                 computations. Most of these methods have satisfactory
                 results only in laboratory flumes. Due to the ability
                 to model complex phenomena, the artificial intelligence
                 methods have recently been employed for wide
                 applications in various fields of water engineering.
                 Linear genetic programming (LGP), a branch of
                 artificial intelligence methods, is able to optimise
                 the model structure and its components and to derive an
                 explicit equation based on the variables of the
                 phenomena. In this paper, a precise dimensionless
                 equation has been derived for prediction of flood
                 discharge using LGP. The proposed model was developed
                 using published data compiled for stage-discharge data
                 sets for 394 laboratories, and field of 30 compound
                 channels. The results indicate that the LGP model has a
                 better performance than the existing models.",

Genetic Programming entries for Hazi Mohammad Azamathulla Abdulreza Zahiri