Effective 1-day ahead prediction of hourly surface ozone concentrations in eastern Spain using linear models and neural networks

This source preferred by Emili Balaguer-Ballester

Authors: Balaguer-Ballester, E., Camps i Valls, G., Carrasco-Rodriguez, J.L., Soria Olivas, E. and del Valle-Tascon, S.

Journal: Ecological Modelling

Volume: 156

Pages: 27-41

Publisher: Elsevier

DOI: 10.1016/S0304-3800(02)00127-8

This data was imported from Scopus:

Authors: Balaguer Ballester, E., Camps I Valls, G., Carrasco-Rodriguez, J.L., Soria Olivas, E. and Del Valle-Tascon, S.

Journal: Ecological Modelling

Volume: 156

Issue: 1

Pages: 27-41

ISSN: 0304-3800

DOI: 10.1016/S0304-3800(02)00127-8

The aim of this research was to develop pure predictive models in order to provide 24 h advance forecasts of the hourly ozone concentration for the rural site of Carcagente (Valencia, Spain) and the urban sites of Paterna (Valencia, Spain) and Alcoy (Alicante, Spain) over 4 years from 1996 to 1999. The peculiarity of the model presented here is that it uses past and previously predicted information of inputs exclusively, thus being this is the first genuine 24 h advance O3 predictive model with neural networks. We used autoregressive-moving average with exogenous inputs (ARMAX), multilayer perceptrons and FIR neural networks. Five performance measures yield reasonably good results in the three sampling sites. The results indicate that the models developed predict the O3 time series more effectively compared with previous procedures based on dynamical system theory. The neural network's models yield better results than linear models when exogenous inputs are included. The prediction accuracy of these models enables, for the first time, an effective warning to be made in cases where EU public information threshold values are exceeded. © 2002 Elsevier Science B.V. All rights reserved.

This data was imported from Web of Science (Lite):

Authors: Ballester, E.B., Valls, G.C.I., Carrasco-Rodriguez, J.L., Olivas, E.S. and del Valle-Tascon, S.

Journal: ECOLOGICAL MODELLING

Volume: 156

Issue: 1

Pages: 27-41

ISSN: 0304-3800

The data on this page was last updated at 05:12 on February 21, 2020.