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Forecasting the SST Space‐time variability of the Alboran Sea with genetic algorithms
Author(s) -
Álvarez Alberto,
López Cristóbal,
Riera Margalida,
HernándezGarcía Emilio,
Tintoré Joaquín
Publication year - 2000
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/1999gl011226
Subject(s) - empirical orthogonal functions , sea surface temperature , amplitude , nonlinear system , algorithm , climatology , genetic algorithm , computer science , geology , meteorology , geography , machine learning , physics , quantum mechanics
We propose a nonlinear ocean forecasting technique based on a combination of genetic algorithms and empirical orthogonal function (EOF) analysis. The method is used to forecast the space‐time variability of the sea surface temperature (SST) in the Alboran Sea. The genetic algorithm finds the equations that best describe the behaviour of the different temporal amplitude functions in the EOF decomposition and, therefore, enables global forecasting of the future time‐variability.