Prediction of PM10 concentrations for Bahía Blanca, Argentina

PM10 non-traditional modelling for e-government development is described in detail. Ambient PM10 concentrations were predicted using meteorological variables as inputs, whose relevance for a generated Artificial Neural Network was analyzed by a feature selection method. The work is specially focused...

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Detalles Bibliográficos
Autores principales: Brignole, Nélida B., Chiarvetto Peralta, Lucila L., Díaz, Mónica F.
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2012
Materias:
Ciencias Informáticas
Intelligent agents
Neural nets
artificial neural network
particulate matter
PM10
forecast model
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23592
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
Descripción
Sumario:PM10 non-traditional modelling for e-government development is described in detail. Ambient PM10 concentrations were predicted using meteorological variables as inputs, whose relevance for a generated Artificial Neural Network was analyzed by a feature selection method. The work is specially focused on the surroundings of Bahía Blanca city, its petrochemical pole and Ing. White grain port. Its accuracy was tested with time windows ranging from 2004 to 2006. A trustworthy simulation of the physical phenomena was built. As a result, this predictive model will contribute to the local observatory in order to trigger early-alert warnings.

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