Abstract
Atmospheric pollution is a health hazard. Thus, an accurate prediction of atmospheric pollution time series is almost a necessity nowdays. The existence of missing data further complicates this challenging problem. The cubic spline interpolation method is applied on the hourly measurements of nitrogen oxide (NO), nitrogen dioxide (NO 2), ozone (O 3), and dust partides (PM10). In order to asses predictability of an air pollution time series, a class of gradient-descent based neural adaptive filters is employed. Results indicate that, yet simple, this class of neural adaptive filters is a suitable solution.
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© 2001 Springer-Verlag Wien
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Krcmar, I.R., Mandic, D.P., Foxall, R.J. (2001). On Predictability of Atmospheric Pollution Time Series. In: Kůrková, V., Neruda, R., Kárný, M., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6230-9_120
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DOI: https://doi.org/10.1007/978-3-7091-6230-9_120
Publisher Name: Springer, Vienna
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