Letters: Improving the prediction of average total ozone in column over the Iberian Peninsula using neural networks banks

Authors:
S. Salcedo-Sanz;J. L. Camacho;Á. M. Pérez-Bellido;E. G. Ortiz-Garcia;A. Portilla-Figueras;E. Hernández-Martín
Affiliations:
Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain;Spanish Meteorology State Agency (AEMET), Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain;Department of Physics of the Earth, Astronomy and Astrophysics II, Universidad Complutense de Madrid, Spain
Venue:
Neurocomputing
Year:
2011

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Abstract

In this letter we propose a class of neural network banks to improve the performance of average total ozone in column (TOC) prediction, using real satellite data over the Iberian Peninsula. The proposed neural network banks exploit the possibility of separating the average TOC series into its known components, applying different neural networks as input to different structures which form the final bank. These neural network banks have proven to be very effective in the experiments carried out, obtaining important improvements over standard networks in the prediction of average TOC data series over the Iberian Peninsula. Also, we show that this good performance of the neural network banks is maintained when different procedures of deseasonalization are applied to the ozone measure and also to the prediction variables.