PM10forecasting using kernel adaptive filtering: An Italian case study

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 PM10data 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 PM10such as NO2. © Springer-Verlag Berlin Heidelberg 2013.