Comparison between linear genetic programming and M5 tree models to predict flow discharge in compound channels

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@Article{journals/nca/ZahiriA14,
  author =       "A. Zahiri and H. Md. Azamathulla",
  title =        "Comparison between linear genetic programming and {M5}
                 tree models to predict flow discharge in compound
                 channels",
  journal =      "Neural Computing and Applications",
  year =         "2014",
  number =       "2",
  volume =       "24",
  pages =        "413--420",
  keywords =     "genetic algorithms, genetic programming, compound
                 channels, linear genetic programming, m5 tree decision
                 model, stage-discharge curve",
  bibdate =      "2014-01-21",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/nca/nca24.html#ZahiriA14",
  URL =          "http://dx.doi.org/10.1007/s00521-012-1247-0",
  DOI =          "doi:10.1007/s00521-012-1247-0",
  size =         "8 pages",
  abstract =     "There are many studies on the hydraulic analysis of
                 steady uniform flows in compound open channels. Based
                 on these studies, various methods have been developed
                 with different assumptions. In general, these methods
                 either have long computations or need numerical
                 solution of differential equations. Furthermore, their
                 accuracy for all compound channels with different
                 geometric and hydraulic conditions may not be
                 guaranteed. In this paper, to overcome theses
                 limitations, two new and efficient algorithms known as
                 linear genetic programming (LGP) and M5 tree decision
                 model have been used. In these algorithms, only three
                 parameters (e.g., depth ratio, coherence, and ratio of
                 computed total flow discharge to bank full discharge)
                 have been used to simplify its applications by
                 hydraulic engineers. By compiling 394 stage-discharge
                 data from laboratories and fields of 30 compound
                 channels, the derived equations have been applied to
                 estimate the flow conveyance capacity. Comparison of
                 measured and computed flow discharges from LGP and M5
                 revealed that although both proposed algorithms have
                 considerable accuracy, LGP model with R-squared = 0.98
                 and RMSE = 0.32 has very good performance.",
}

Genetic Programming entries for Abdulreza Zahiri Hazi Mohammad Azamathulla

Citations