Prediction Of The Next Day Maximum Ozone
Concentration Using Multiple Linear And
Principal Component Regressions
Author(s)
S. I. V. Sousa, F. G. Martins, M. C. Pereira
& M. C. M. Alvim-Ferraz
Abstract
Prediction of ground-level ozone concentrations is very important due to the
negative impacts of this pollutant on human health and environment. Multiple
linear regression (MLR) and principal component (PCR) regression were used as
statistical models for the forecasting of ozone concentrations. The aim of this
study was to predict the next day maximum ozone concentration. The studies
were performed considering separately the year 2002 and the respective four
trimesters. A subset of the last 10 and 30 days was used, respectively, for each
trimester and for the year to validate the models. The predictor variables were
inferred by the analysis of the linear correlation with ozone. For that, maxima
hourly values for ozone, ratio of nitrogen dioxide and nitrogen monoxide,
temperature, wind velocity and the minima hourly values for carbon monoxide
and relative humidity were used. The main results achieved were: i) the
performance indexes obtained for validation datasets were usually higher with
PCR; ii) the number of principal components considered to develop the PCR was
dependent of the dataset considered; iii) the PCR is more robust than MLR
because collinearity effects are accounted with the first approach; iv) PCR model
is shown to be a useful tool to provide protection for the public, through the use
of early warnings for the population.
Keywords: ground-level ozone forecasting, multiple linear regression, principal
component analysis, principal component regression.
Keywords
ground-level ozone forecasting, multiple linear regression, principal
component analysis, principal component regression.
Related Book
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