Valid predictions with confidence estimation in an air pollution problem

The present study is aimed to evaluate levels of air pollution for the Barcelona Metropolitan Region. For this purpose, a newly developed approach called conformal predictors is considered, and, in particular, use is made of the ridge regression confidence machine (RRCM). The hallmark of this method is that it gives valid estimates, i.e. for a given level of significance of prediction, the probability of error does not exceed this level. Moreover, the chosen specification of the RRCM predictor does not place any requirements on data distribution, apart from being independent and identically distributed. A linear ridge regression conformal predictor has been applied to the data. It has allowed to obtain valid interval estimates of annual nitrogen dioxide concentrations with 95 % confidence. The model has provided good results, but to further increase the efficiency of prediction, the RBF kernel has been used. The data for this study have been provided by the XVPCA (Network for Monitoring and Forecasting of Air Pollution) of the Generalitat of Catalonia. The pollutant considered in this paper is nitrogen dioxide. Its values are represented by annual average concentrations within the period from 1998 to 2009. This paper also describes an application of ordinary kriging, and its results have been compared to those of ridge regression conformal predictor.

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