Development Of Wave Buoy Network Using Soft Computing Techniques

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

@InProceedings{Londhe:2008:OCEANS,
  author =       "Shreenivas N. Londhe",
  title =        "Development Of Wave Buoy Network Using Soft Computing
                 Techniques",
  booktitle =    "OCEANS 2008 - MTS/IEEE Kobe Techno-Ocean",
  year =         "2008",
  month =        "8-11 " # apr,
  pages =        "1--8",
  address =      "Kobe, Japan",
  keywords =     "genetic algorithms, genetic programming, AD 2002 to
                 2004, Artificial Neural Networks, Australia, Canada,
                 Germany, Gulf of Mexico, India, UK, USA, buoy programs,
                 ocean wave buoys network development, ocean wave data
                 measurements, soft computing techniques, stochastic
                 techniques, geophysics computing, neural nets, ocean
                 waves, oceanographic techniques, stochastic processes",
  DOI =          "doi:10.1109/OCEANSKOBE.2008.4530913",
  abstract =     "Wave buoys are perhaps the only reliable source
                 measuring waves continuously for years. This is perhaps
                 the most vital reason for establishment of data buoy
                 programs by various countries like USA (NDBC),
                 Australia, Canada, UK, Germany, India (NDBP) etc. The
                 wave data measurements not only provide real time wave
                 information for Coastal and Ocean related activities
                 but also form wave data base useful for predicting
                 future events using statistical or stochastic
                 techniques. However some times these wave buoys stop
                 functioning either due to malfunctioning instruments or
                 maintenance-related reasons resulting into loss of
                 data. This paper presents use of soft computing
                 techniques like Artificial Neural Networks (ANN) and
                 Genetic Programming (GP) to retrieve this lost data by
                 forming a network of wave buoys in a region. For
                 developing the buoy network common data of hourly
                 significant wave heights at six buoys in the Gulf of
                 Mexico namely 42001, 42003, 42007, 42036, 42039 and
                 42040 for the years 2002 and 2004 is used. A separate
                 network for each buoy is developed as the 'target buoy'
                 with other 5 buoys as 'input buoys' which can be
                 operated to retrieve lost data at a location. The
                 testing results of both approaches when compared showed
                 superiority of Genetic Programming over Artificial
                 Neural Network as evident by higher correlation
                 coefficient between observed and predicted wave heights
                 in all cases. The wave height plots also pointed out
                 that GP estimates wave heights in extreme events
                 (peaks) more accurately than ANN.",
  notes =        "Also known as \cite{4530913}",
}

Genetic Programming entries for S N Londhe

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