Air Quality Forecasting Using Neural Networks

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Conference article in proceedings
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Date
2017-02-09
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en
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Proc. of the 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), Athens, Greece, December 6-9, 2016.
Abstract
In this paper, a neural network approach is proposed for air quality forecasting based on the air quality time series itself as well as external meteorological records. A regularized version of the Extreme Learning Machine is used as the main model for the forecasts and feature selection is performed to select the most relevant model inputs. The proposed method is evaluated under different approaches for performing spatial data fusion. Experiments show that accuracy is increased by considering meteorological data; that it matters greatly for the model how the spatial aspect of the problem is taken into account; and finally, that the model is generally able to select relevant inputs and provide accurate air quality forecasts.
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Zhao , C , van Heeswijk , M & Karhunen , J 2017 , Air Quality Forecasting Using Neural Networks . in 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 . , 7850128 , IEEE , IEEE Symposium Series on Computational Intelligence , Athens , Greece , 06/12/2016 . https://doi.org/10.1109/SSCI.2016.7850128