A 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions

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dc.creatorDutot, A.
dc.creatorRynkiewicz, Joseph
dc.creatorSteiner, F.
dc.creatorRude, J.
dc.date2008-02-27
dc.date.accessioned2026-07-07T09:23:33Z
dc.date.available2026-07-07T09:23:33Z
dc.descriptionA neural network combined to a neural classifier is used in a real time forecasting of hourly maximum ozone in the centre of France, in an urban atmosphere. This neural model is based on the MultiLayer Perceptron (MLP) structure. The inputs of the statistical network are model output statistics of the weather predictions from the French National Weather Service. With this neural classifier, the Success Index of forecasting is 78% whereas it is from 65% to 72% with the classical MLPs. During the validation phase, in the Summer of 2003, six ozone peaks above the threshold were detected. They actually were seven.
dc.identifierhttps://arxiv.org/abs/0802.3969
dc.identifierhttp://arxiv.org/abs/0802.3969
dc.identifierEnvironmental Modelling and Software 22, 9 (2007) 1261-1269
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/155771
dc.subjectApplications
dc.subjectStatistics Theory
dc.titleA 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions
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