Air Quality Forecasting Using Neural Networks

Cheng Zhao, Mark van Heeswijk, Juha Karhunen

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

3 Sitaatiot (Scopus)
154 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
Otsikko2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
KustantajaIEEE
Sivumäärä7
ISBN (elektroninen)9781509042401
DOI - pysyväislinkit
TilaJulkaistu - 9 helmikuuta 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE Symposium Series on Computational Intelligence - Athens, Kreikka
Kesto: 6 joulukuuta 20169 joulukuuta 2016

Conference

ConferenceIEEE Symposium Series on Computational Intelligence
LyhennettäSSCI
MaaKreikka
KaupunkiAthens
Ajanjakso06/12/201609/12/2016

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  • Siteeraa tätä

    Zhao, C., van Heeswijk, M., & Karhunen, J. (2017). Air Quality Forecasting Using Neural Networks. teoksessa 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 [7850128] IEEE. https://doi.org/10.1109/SSCI.2016.7850128