Version améliorée du papier de la conférence EENVIRO dans "Mathematical Modelling in Civil Engineering"

URBAN OZONE CONCENTRATION FORECASTING WITH ARTIFICIAL NEURAL NETWORK IN CORSICA
Wani Tamas, Gilles Notton, Christophe Paoli, Cyril Voyant, Marie-Laure Nivet, Aurelia Balu


A la suite de la conférence de Bucarest, les organisateur ont proposé à Wani Tamas de soumettre une version améliorée de son travail. Dans ce dernier, on y trouve des résultats liés à l'utilisation d'outils classiques de prévision de séries temporelles appliqués au cas des polluants atmosphériques. Des futures améliorations des méthodologies de prédictions sont en cours d'expérimentation et devraient permettre d'améliorer encore la qualité des résulats.
Le résumé de ce papier est:
"Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air quality in Corsica (France), needs to develop a short-term prediction model to lead its mission of information towards the public. Various deterministic models exist for local forecasting, but need important computing resources, a good knowledge of atmospheric processes and can be inaccurate because of local
climatical or geographical particularities, as observed in Corsica, a mountainous island located in the Mediterranean Sea. As a result, we focus in this study on statistical models, and particularly Artificial Neural Networks (ANNs) that have shown good results in the prediction of ozone concentration one hour ahead with data measured locally. The purpose of this study is to build a predictor realizing predictions of ozone 24 hours ahead in Corsica in order to be able to anticipate pollution peaks formation and to take appropriate preventive measures. Specific meteorological
conditions are known to lead to particular pollution event in Corsica (e.g. Saharan dust events). Therefore, an ANN model will be used with pollutant and meteorological data for operational forecasting. Index of agreement of this model was calculated with a one year test dataset and reached 0.88."

http://mmce.rs.utcb.ro/component/content/article/18-2014/49-scientific-journal-mathematical-modeling-no-1-2014.html http://mmce.rs.utcb.ro/component/content/article/18-2014/49-scientific-journal-mathematical-modeling-no-1-2014.html


Rédigé par Cyril VOYANT le Samedi 5 Avril 2014 à 11:33 | Lu 265 fois