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Application of air quality combination forecasting to Bogota

journal contribution
posted on 2014-06-01, 00:00 authored by Joakim WesterlundJoakim Westerlund, J-P Urbain, J Bonilla
The bulk of existing work on the statistical forecasting of air quality is based on either neural networks or linear regressions, which are both subject to important drawbacks. In particular, while neural networks are complicated and prone to in-sample overfitting, linear regressions are highly dependent on the specification of the regression function. The present paper shows how combining linear regression forecasts can be used to circumvent all of these problems. The usefulness of the proposed combination approach is verified using both Monte Carlo simulation and an extensive application to air quality in Bogota, one of the largest and most polluted cities in Latin America. © 2014 Elsevier Ltd.

History

Journal

Atmospheric environment

Volume

89

Pagination

22-28

Location

Amsterdam, The Netherlands

ISSN

1873-2844

eISSN

1352-2310

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal, C Journal article

Copyright notice

2014, Elsevier

Publisher

Elsevier