Fourier-based optimization for multivariate spatial-temporal regression model in chlorophyll-a presence prediction around Galápagos Islands
DOI:
https://doi.org/10.18779/ingenio.v6i1.561Keywords:
Spatial temporal regression, Illegal fishing prevention, Discrete Fourier transform, biogeochemical ocean variablesAbstract
Chlorophyll-a (Chl-a) is an indicator of phytoplankton biomass, which can be used to predict the presence of fish in the ocean. By predicting the Chl-a with sufficient time, this data can be used to better plan naval operations that combat illegal, unreported and unregulated fishing by increasing surveillance of the identified areas where the greatest fishing activity would take place. In this work, a new technique is proposed, based on the application of the discrete Fourier transform theory to develop multivariate spatial-temporal regression model, which considers physical and biogeochemical ocean variables to predict the presence of Chlorophyll-a around Galápagos Islands. This work considers open access data taken from the Copernicus space program, used in the European Union.
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