• DocumentCode
    65825
  • Title

    Empirical Forecasting of HF-Radar Velocity Using Genetic Algorithms

  • Author

    Orfila, Alejandro ; Molcard, Anne ; Sayol, Juan M. ; Marmain, Julien ; Bellomo, Lucio ; Quentin, Celine ; Barbin, Yves

  • Author_Institution
    Dept. of Marine Technol. & Operational Oceanogr., UIB, Palma de Mallorca, Spain
  • Volume
    53
  • Issue
    5
  • fYear
    2015
  • fDate
    May-15
  • Firstpage
    2875
  • Lastpage
    2886
  • Abstract
    We present a coastal ocean current forecasting system using exclusively past observations of a high-frequency radar (HF-Radar). The forecast is made by developing a new approach based on physical and mathematical results of the nonlinear dynamical systems theory that allows to obtain a predictive equation for the currents. Using radial velocities from two HF-Radar stations, the spatiotemporal variability of the fields is first decomposed using the empirical orthogonal functions. The amplitudes of the most relevant modes representing their temporal evolution are then approximated with functions obtained through a genetic algorithm. These functions will be then combined to obtain the hourly currents at the area for the next 36 h. The results indicate that after 4 h and for a horizon of 24 h, the computed predictions provide more accurate current fields than the latest available field (i.e., persistent field).
  • Keywords
    genetic algorithms; marine radar; mathematical analysis; nonlinear dynamical systems; oceanographic equipment; oceanographic techniques; HF-radar velocity forecasting; coastal ocean current forecasting system; empirical orthogonal function; genetic algorithm; high-frequency radar; mathematical analysis; nonlinear dynamical system theory; predictive equation; temporal evolution; time 24 hour; time 36 h; time 4 h; Equations; Forecasting; Genetic algorithms; Radar; Sea measurements; Sea surface; Empirical modeling; high-frequency radar (HF-Radar); operational oceanography;
  • fLanguage
    English
  • Journal_Title
    Geoscience and Remote Sensing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0196-2892
  • Type

    jour

  • DOI
    10.1109/TGRS.2014.2366294
  • Filename
    6971122