Ozone forecasting using an online updating Gaussian-process model Online publication date: Tue, 02-Feb-2016
by Dejan Petelin; Primož Mlakar; Marija Zlata Božnar; Boštjan Grašič; Juš Kocijan
International Journal of Environment and Pollution (IJEP), Vol. 57, No. 3/4, 2015
Abstract: Forecasting the ozone concentration and informing populations about times when air-quality standards are not being met is an important task. One of the possibilities for carrying out such forecasting is Gaussian-process (GP) models and artificial neural networks, both of which can make a forecast using available present-time or historical measurements of air-pollution or meteorological parameters at the location of automatic air-quality measuring stations. In this paper an on-line updating, or evolving, GP model is evaluated. Its main advantage is an ability to learn with almost no prior knowledge or data. This means that it can be used for modelling a range of variables shortly after the measurement station is established. The evolving GP model for ozone forecasting is compared to the full GP model and the multilayer-perceptron neural networks model. The investigation shows that the evolving GP model performs sufficiently well for it to be used for informing citizens when alarm-level concentrations occur.
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