Combination of artificial neural network models for air quality predictions for the region of Annaba, Algeria
This paper describes the development of a global air quality prediction model based on the combination of five different pollutants predicted values; specifically: O3, PM10, SO2, NOx and COx. Each pollutant concentration prediction
is obtained from a radial basis function (RBF) neural network developed in order to predict 12 hours ahead the five air pollutant parameters for the region of Annaba, northeastern Algeria. Given the measurement of air pollutant concentration and three chosen metrological parameters (wind speed,
temperature and humidity) at time t, the models can predict the air pollutant concentrations at t+12 hours. Once these concentrations are obtained, a second artificial neural network (ANN) given by a multi-layered perceptron (MLP) is used to combine them and forecast the air
quality over a scale ranging from 1 for very good to 5 for very bad.
Keywords: Air quality prediction; Artificial neural network
Document Type: Research Article
Affiliations: Laboratoire de Gestion Electronique de Documents, Department of Computer Science,University Badji Mokhtar, PO-Box 1223000,Annaba, Algeria
Publication date: 01 February 2012
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