| Abstract: | Over the past years, the health impact of airborne particulate matter (mathrm{PM}_{10}) has become a very topical subject. Thereby, a lot of research effort in the environmental sciences goes towards the modeling and the prediction of ambient (mathrm{PM}_{10}) concentrations. In this paper, we are interested in the statistical classification of the daily mean (mathrm{PM}_{10}) concentration in Tunisia according to the authority regulation. We consider two monitoring stations: a big industrial station and a traffic station. The main goal of this work is to determine the pertinent predictors of (mathrm{PM}_{10}) concentration within a nonlinear multiclass framework. To do this, we used two popular statistical learning methods; the support vector machines (SVM) and the random forests (RF). The statistical results obtained on the real datasets, show that RF outperform SVM for the purpose of variable selection even with a reduced number of observations compared to the number of explicative variables. It was also demonstrated that the (mathrm{PM}_{10}) concentration measured yesterday is the most relevant predictor of its present-day value. Moreover, we found that the more delayed values of (mathrm{PM}_{10}) concentration may be crucial to get an accurate prediction. |
