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. © Springer-Verlag Berlin Heidelberg 2013.
PM10 forecasting using kernel adaptive filtering: An Italian case study / Scardapane, Simone; Comminiello, Danilo; Scarpiniti, Michele; Parisi, Raffaele; Uncini, Aurelio. - 19(2013), pp. 93-100. - SMART INNOVATION, SYSTEMS AND TECHNOLOGIES. [10.1007/978-3-642-35467-0_10].
PM10 forecasting using kernel adaptive filtering: An Italian case study
SCARDAPANE, SIMONE;COMMINIELLO, DANILO;SCARPINITI, MICHELE;PARISI, Raffaele;UNCINI, Aurelio
2013
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. © Springer-Verlag Berlin Heidelberg 2013.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
