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Regressor selection for ozone prediction
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DOI：10.1016/j.simpat.2015.03.004.png)
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Abstract
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Being able to predict high concentrations of tropospheric ozone is important because of its negative impact on human health. In this paper eight regressor-selection methods are utilised in a case study for ozone prediction in the city of Nova Gorica, Slovenia. The comparison of the selected methods proved to be useful for building models that successfully predict the ozone concentrations for the treated case. Different regressors are selected for different models, with different methods based on the validation procedure's cost functions. Namely, for the model to predict the maximum daily ozone concentration, ten regressors are selected; for the average concentration of ozone between 8.00 and 20.00 h, fifteen regressors are selected; and for the average daily concentration, ten regressors are selected. The result of the study is a regressor selection that is specific for a particular geographical location. Moreover, the study reveals that regressor selection, as well as the obtained models, differ depending on the kind of averaging interval of the ozone concentration. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Regressor selection
Regression modelling
Black-box modelling
Prediction of ozone concentration
Dynamical systems
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