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Advances in Meteorology
Volume 2013, Article ID 538508, 15 pages
http://dx.doi.org/10.1155/2013/538508
Research Article

One-Day Prediction of Biometeorological Conditions in a Mediterranean Urban Environment Using Artificial Neural Networks Modeling

1Department of Mechanical Engineering, Technological Educational Institute of Piraeus, 250 Thivon and P. Ralli Street, 122 44 Aegaleo, Greece
2Laboratory of Climatology and Atmospheric Environment, Faculty of Geology and Geoenvironment, University of Athens, Panepistimiopolis, 157 84 Athens, Greece
3General Department of Mathematics, Technological Educational Institute of Piraeus, 250 Thivon and P. Ralli Street, 122 44 Aegaleo, Greece

Received 13 June 2013; Revised 11 August 2013; Accepted 5 September 2013

Academic Editor: Andreas Matzarakis

Copyright © 2013 K. P. Moustris et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The present study, deals with the 24-hour prognosis of the outdoor biometeorological conditions in an urban monitoring site within the Greater Athens area, Greece. For this purpose, artificial neural networks (ANNs) modelling techniques are applied in order to predict the maximum and the minimum value of the physiologically equivalent temperature (PET) one day ahead as well as the persistence of the hours with extreme human biometeorological conditions. The findings of the analysis showed that extreme heat stress appears to be 10.0% of the examined hours within the warm period of the year, against extreme cold stress for 22.8% of the hours during the cold period of the year. Finally, human thermal comfort sensation accounts for 81.8% of the hours during the year. Concerning the PET prognosis, ANNs have a remarkable forecasting ability to predict the extreme daily PET values one day ahead, as well as the persistence of extreme conditions during the day, at a significant statistical level of .