@article {4968,
	title = {Application of multiple linear regression models and artificial neural networks on the surface ozone forecast in the greater Athens Area, Greece},
	journal = {Advances in Meteorology},
	volume = {2012},
	year = {2012},
	note = {Cited By :4Export Date: 2 November 2015Correspondence Address: Nastos, P.T.; Laboratory of Climatology and Atmospheric Environment, University of Athens, University Campus, Panepistimiopolis GR 157 84, Athens, Greece; email: nastos@geol.uoa.grReferences: Lu, W.Z., Fan, H.Y., Leung, A.Y.T., Wong, J.C.K., Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization (2002) Environmental Monitoring and Assessment, 79 (3), pp. 217-230. , 2-s2.0-0036837418 10.1023/A:1020274409612;Lippmann, M., Health effects of tropospheric ozone (1991) Environmental Science and Technology, 25 (12), pp. 1954-1962. , 2-s2.0-0026359463; 
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},
	month = {2012},
	abstract = {An attempt is made to forecast the daily maximum surface ozone concentration for the next 24 hours, within the greater Athens area (GAA). For this purpose, we applied Multiple Linear Regression (MLR) models against a forecasting model based on Artificial Neural Network (ANN) approach. The availability of basic meteorological parameters is of great importance in order to forecast the ozone{\textquoteright}s concentration levels. Modelling was based on recorded meteorological and air pollution data from thirteen monitoring sites within the GAA (network of the Hellenic Ministry of the Environment, Energy and Climate Change) over five years from 2001 to 2005. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that in every aspect, the prognostic model by far is the ANN model. This suggests that the ANN model can be used to issue warnings for the general population and mainly sensitive groups. {\textcopyright} 2012 K. P. Moustris et al.},
	isbn = {16879309 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84874911486\&partnerID=40\&md5=09f301116f2bdba48cfdacd9d5b07fcf},
	author = {Moustris, K. P. and Nastos, P. T. and Larissi, I. K. and Paliatsos, A. G.}
}
