Air Quality Forecasting Using Neural Networks

Cheng Zhao, Mark van Heeswijk, Juha Karhunen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

9 Citations (Scopus)
197 Downloads (Pure)


In this paper, a neural network approach is proposed for air quality forecasting based on the air quality time series itself as well as external meteorological records. A regularized version of the Extreme Learning Machine is used as the main model for the forecasts and feature selection is performed to select the most relevant model inputs. The proposed method is evaluated under different approaches for performing spatial data fusion. Experiments show that accuracy is increased by considering meteorological data; that it matters greatly for the model how the spatial aspect of the problem is taken into account; and finally, that the model is generally able to select relevant inputs and provide accurate air quality forecasts.
Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Number of pages7
ISBN (Electronic)9781509042401
Publication statusPublished - 9 Feb 2017
MoE publication typeA4 Article in a conference publication
EventIEEE Symposium Series on Computational Intelligence - Athens, Greece
Duration: 6 Dec 20169 Dec 2016


ConferenceIEEE Symposium Series on Computational Intelligence
Abbreviated titleSSCI


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