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Empirical Forecasting of HF-Radar Velocity Using Genetic Algorithms

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).
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Submitted on : Wednesday, January 28, 2015 - 5:33:15 PM
Last modification on : Thursday, October 15, 2020 - 4:32:47 PM

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Alejandro Orfila, Anne Molcard, Juan Sayol, Julien Marmain, Lucio Bellomo, et al.. Empirical Forecasting of HF-Radar Velocity Using Genetic Algorithms. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2015, 53 (05), pp.2875-2886. ⟨10.1109/TGRS.2014.2366294⟩. ⟨hal-01110709⟩



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