Revisiting urban air quality forecasting: a regression approach

Abstract We address air quality (AQ) forecasting as a regression problem employing computational intelligence (CI) methods for the Gdańsk Metropolitan Area (GMA) in Poland and the Thessaloniki Metropolitan Area (TMA) in Greece. Linear Regression as well as Artificial Neural Network models are developed, accompanied by Random Forest models, for five locations per study area and for a dataset of limited feature dimensionality. An ensemble approach is also used for generating and testing AQ forecasting models. Results indicate good model performance with a correlation coefficient between forecasts and measurements for the daily mean $$\hbox {PM}_{10}$$ PM10 concentration one day in advance reaching 0.765 for one of the TMA locations and 0.64 for one of the GMA locations. Overall results suggest that the specific modelling approach can support the provision of air quality forecasts on the basis of limited feature space dimensionality and by employing simple linear regression models.

Abstract licence: CC BY

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