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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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