Alternative data-driven methods to estimate wind from waves by inverse modeling

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@Article{Daga:2009:NH,
  author =       "Mansi Daga and M. C. Deo",
  title =        "Alternative data-driven methods to estimate wind from
                 waves by inverse modeling",
  journal =      "Natural Hazards",
  year =         "2009",
  volume =       "49",
  number =       "2",
  pages =        "293--310",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, Locally
                 weighted learning, Model trees, Inverse modeling, Wind
                 estimation, LWOR, MT, GP",
  ISSN =         "0921-030X",
  DOI =          "doi:10.1007/s11069-008-9299-2",
  size =         "18 pages",
  abstract =     "An attempt is made to derive wind speed from wave
                 measurements by carrying out an inverse modeling. This
                 requirement arises out of difficulties occasionally
                 encountered in collecting wave and wind data
                 simultaneously. The wind speed at every 3-h interval is
                 worked out from corresponding simultaneous measurements
                 of significant wave height and average wave periods
                 with the help of alternative data-driven methods such
                 as program-based genetic programming, model trees, and
                 locally weighted projection regression. Five different
                 wave buoy locations in Arabian Sea, representing
                 nearshore and offshore as well as shallow and deep
                 water conditions, are considered. The duration of
                 observations ranged from 15 months to 29 months for
                 different sites. The testing performance of calibrated
                 models has been evaluated with the help of eight
                 alternative error statistics, and the best model for
                 all locations is determined by averaging out the error
                 measures into a single evaluation index. All the three
                 methods satisfactorily estimated the wind speed from
                 known wave parameters through inverse modeling. The
                 genetic programming is found to be the most suitable
                 tool in majority of the cases.",
  notes =        "Discussed by \cite{Gandomi:2010:NH}

                 Discipulus. Goa, Minicoy Island, Marmagoa. Storm
                 modelling

                 Department of Civil Engineering, Indian Institute of
                 Technology, Bombay, Mumbai, 400076, India",
}

Genetic Programming entries for Mansi Daga M C Deo

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