Abstract
Short term prediction of air pollution is gaining increasing attention in the research community, due to its social and economical impact. In this paper we study the application of a Kernel Adaptive Filtering (KAF) algorithm to the problem of predicting PM10 data in the Italian province of Ancona, and we show how this predictor is able to achieve a significant low error with the inclusion of chemical data correlated with the PM10 such as NO2.
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Scardapane, S., Comminiello, D., Scarpiniti, M., Parisi, R., Uncini, A. (2013). PM10 Forecasting Using Kernel Adaptive Filtering: An Italian Case Study. In: Apolloni, B., Bassis, S., Esposito, A., Morabito, F. (eds) Neural Nets and Surroundings. Smart Innovation, Systems and Technologies, vol 19. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35467-0_10
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DOI: https://doi.org/10.1007/978-3-642-35467-0_10
Publisher Name: Springer, Berlin, Heidelberg
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