Valid predictions with confidence estimation in an air pollution problem. / Ivina, Olga; Nouretdinov, Ilia; Gammerman, Alex.

In: Progress in Artificial Intelligence, Vol. 1, 17.06.2012, p. 235-243.

Research output: Contribution to journalArticlepeer-review



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
O. Ivina (B)
Research Group on Statistics, Applied Economics and Health
(GRECS), Universitat de Girona, Girona, Spain
O. Ivina
CIBER of Epidemiology and Public Health (CIBERESP),
Granada, Spain
I. Nouretdinov · A. Gammerman
Computer Learning Research Centre, Royal Holloway
University of London, Surrey, UK
A. Gammerman
describes an application of ordinary kriging, and its results
have been compared to those of ridge regression conformal
Original languageEnglish
Pages (from-to)235-243
Number of pages9
JournalProgress in Artificial Intelligence
Publication statusPublished - 17 Jun 2012
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 16819734