Filling up gaps in wave data with genetic programming

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

@Article{Ustoorikar2008177,
  author =       "Ketaki Ustoorikar and M. C. Deo",
  title =        "Filling up gaps in wave data with genetic
                 programming",
  journal =      "Marine Structures",
  volume =       "21",
  number =       "2-3",
  pages =        "177--195",
  year =         "2008",
  ISSN =         "0951-8339",
  DOI =          "doi:10.1016/j.marstruc.2007.12.001",
  URL =          "http://www.sciencedirect.com/science/article/B6V41-4RR20Y8-1/2/76cfad2398264322e376b67c08880225",
  keywords =     "genetic algorithms, genetic programming, Data gaps,
                 Neural networks, Wave heights",
  abstract =     "A given time series of significant wave heights
                 invariably contains smaller or larger gaps or missing
                 values due to a variety of reasons ranging from
                 instrument failures to loss of recorders following
                 human interference. In-filling of missing information
                 is widely reported and well documented for variables
                 like rainfall and river flow, but not for the wave
                 height observations made by rider buoys. This paper
                 attempts to tackle this problem through one of the
                 latest soft computing tools, namely, genetic
                 programming (GP). The missing information in hourly
                 significant wave height observations at one of the data
                 buoy stations maintained by the US National Data Buoy
                 Center is filled up by developing GP models through
                 spatial correlations. The gap lengths of different
                 orders are artificially created and filled up by
                 appropriate GP programs. The results are also compared
                 with those derived using artificial neural networks
                 (ANN). In general, it is found that the in-filling done
                 by GP rivals that by ANN and many times becomes more
                 satisfactory, especially when the gap lengths are
                 smaller. Although the accuracy involved reduces as the
                 amount of gap increases, the missing values for a long
                 duration of a month or so can be filled up with a
                 maximum average error up to 0.21m in the high seas.",
}

Genetic Programming entries for Ketaki Shirish Ustoorikar M C Deo

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