Evaluation of the temporal scaling variability in forecasting ground-level ozone concentrations obtained from multiple linear regressions.
Ozone is a highly unpredictable pollutant which severely affects living conditions in urban and surrounding areas in the Mediterranean basin. This secondary pollutant periodically reaches extremely high concentrations, damaging human health. Multiple linear regression has been widely used in previous works due to the fact that it is a simple and versatile method for forecasting ozone concentrations. However, these models usually prove their validity using fulfillment of statistical constraints, ignoring other intrinsic characteristics existing in the time series, such as the temporal scaling behavior and the data distribution over different time scales. In previous works, it has been demonstrated that observed ozone time series are of a multifractal nature, meaning that the data distribution can be described by using the multifractal spectrum. This work focuses on the capacity of a forecasting model to reproduce the scaling features existing in an observed time series when several chemical and meteorological explanatory variables are introduced following the stepwise procedure. A comparison between the observed spectrum and the simulated ones for each step is used to check which explanatory variables better reproduce the multifractal nature in real ozone time series. It has been confirmed that a model with few explanatory variables allows reproducing the multifractal nature in the simulated time series with an acceptable accuracy without compromising the values of the coefficient of determination and root-mean-squared error, which were used as performance indicators.
Department of Graphics Engineering and Geomatics, University of Córdoba, Gregor Mendel Building, 3rd Floor, Campus Rabanales, 14071 Cordova, Spain. g22padop@uco.es
P
P
Pavón-Domínguez
F J
FJ
Jiménez-Hornero
E Gutiérrez
EG
de Ravé
D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000706:statistics & numerical data*; D004784:Environmental Monitoring / Q000379:methods*; D005544:Forecasting; D016014:Linear Models; D008956:Models, Chemical; D010126:Ozone / Q000032:analysis*
D000393:Air Pollutants; D010126:Ozone