Real time wave forecasting using wind time history and numerical model

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  author =       "Pooja Jain and M. C. Deo and G. Latha and 
                 V. Rajendran",
  title =        "Real time wave forecasting using wind time history and
                 numerical model",
  journal =      "Ocean Modelling",
  volume =       "36",
  number =       "1-2",
  pages =        "26--39",
  year =         "2011",
  ISSN =         "1463-5003",
  DOI =          "doi:10.1016/j.ocemod.2010.07.006",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 neural networks, Model trees, Wave prediction,
                 Numerical wave prediction",
  abstract =     "Operational activities in the ocean like planning for
                 structural repairs or fishing expeditions require real
                 time prediction of waves over typical time duration of
                 say a few hours. Such predictions can be made by using
                 a numerical model or a time series model employing
                 continuously recorded waves. This paper presents
                 another option to do so and it is based on a different
                 time series approach in which the input is in the form
                 of preceding wind speed and wind direction
                 observations. This would be useful for those stations
                 where the costly wave buoys are not deployed and
                 instead only meteorological buoys measuring wind are
                 moored. The technique employs alternative artificial
                 intelligence approaches of an artificial neural network
                 (ANN), genetic programming (GP) and model tree (MT) to
                 carry out the time series modelling of wind to obtain
                 waves. Wind observations at four offshore sites along
                 the east coast of India were used. For calibration
                 purpose the wave data was generated using a numerical
                 model. The predicted waves obtained using the proposed
                 time series models when compared with the numerically
                 generated waves showed good resemblance in terms of the
                 selected error criteria. Large differences across the
                 chosen techniques of ANN, GP, MT were not noticed. Wave
                 hindcasting at the same time step and the predictions
                 over shorter lead times were better than the
                 predictions over longer lead times. The proposed method
                 is a cost effective and convenient option when a
                 site-specific information is desired.",

Genetic Programming entries for Pooja Jain M C Deo G Latha V Rajendran