Application of evolutionary computation on ensemble forecast of quantitative precipitation

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@Article{DUFEK2017139,
  author =       "Amanda S. Dufek and Douglas A. Augusto and 
                 Pedro L. S. Dias and Helio J. C. Barbosa",
  title =        "Application of evolutionary computation on ensemble
                 forecast of quantitative precipitation",
  journal =      "Computers {\&} Geosciences",
  year =         "2017",
  volume =       "106",
  pages =        "139--149",
  keywords =     "genetic algorithms, genetic programming, Ensemble
                 weather forecast, Quantitative precipitation,
                 Evolutionary computation",
  ISSN =         "0098-3004",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0098300417306507",
  DOI =          "doi:10.1016/j.cageo.2017.06.011",
  abstract =     "An evolutionary computation algorithm known as genetic
                 programming (GP) has been explored as an alternative
                 tool for improving the ensemble forecast of 24-h
                 accumulated precipitation. Three GP versions and six
                 ensembles' languages were applied to several real-world
                 datasets over southern, south-eastern and central
                 Brazil during the rainy period from October to February
                 of 2008–2013. According to the results, the GP
                 algorithms performed better than two traditional
                 statistical techniques, with errors 27–57percent
                 lower than simple ensemble mean and the MASTER super
                 model ensemble system. In addition, the results
                 revealed that GP algorithms outperformed the best
                 individual forecasts, reaching an improvement of
                 34–42percent. On the other hand, the GP algorithms
                 had a similar performance with respect to each other
                 and to the Bayesian model averaging, but the former are
                 far more versatile techniques. Although the results for
                 the six ensembles languages are almost
                 indistinguishable, our most complex linear language
                 turned out to be the best overall proposal. Moreover,
                 some meteorological attributes, including the weather
                 patterns over Brazil, seem to play an important role in
                 the prediction of daily rainfall amount.",
}

Genetic Programming entries for Amanda Sabatini Dufek Douglas A Augusto Pedro Leite da Silva Dias Helio J C Barbosa

Citations