Air quality forecasting using neural networks

2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

Volumn , Issue , 2017, Pages

  Zhao, Chen ^a   Van Heeswijk, Mark ^a   Karhunen, Juha ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

AIR QUALITY; ARTIFICIAL INTELLIGENCE; DATA FUSION; FORECASTING; LEARNING SYSTEMS; METEOROLOGY;

AIR QUALITY FORECASTING; AIR QUALITY FORECASTS; EXTREME LEARNING MACHINE; METEOROLOGICAL DATA; METEOROLOGICAL RECORDS; MODEL INPUTS; SPATIAL ASPECT; SPATIAL DATA;

WEATHER FORECASTING;

EID: 85016050199     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SSCI.2016.7850128     Document Type: Conference Paper

References (17)

1. Air Quality Database, http://www.ilmanlaatu.fi/.
2. W. Deng, Q. Zheng, and L. Chen. Regularized Extreme Learning Machine. In IEEE Symposium on Comp. Intelligence and Data Mining, pages 389-395, 2009.
3. X. Feng, Q. Li, Y. Zhu, J. Hou, L. Jin, and J. Wang. Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmospheric Environment, 107:118-128, 2015.
4. M. W. Gardner and S. R. Dorling. Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London. Atmospheric Environment, 33(5):709-719, 1999.
5. I. Guyon and A. Elisseeff. An introduction to variable and feature selection. Journal of Machine Learning Research, 3:1157-1182, 2003.
6. G. Huang, G.-B. Huang, S. Song, and K. You. Trends in extreme learning machines: A review. Neural Networks, 61:32-48, 1 2015.
7. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew. Extreme learning machine: Theory and applications. Neurocomputing, 70(1-3):489-501, 12 2006.
8. M. Kolehmainen, H. Martikainen, and J. Ruuskanen. Neural networks and periodic components used in air quality forecasting. Atmospheric Environment, 35(5):815-825, 2001.
9. R. H. Myers. Classical and Modern Regression with Applications, 2nd edition. Duxbury, Pacific Grove, CA, USA, 1990.
10. NOAA's National Centers for Environmental Information (NCEI). Hourly/Sub-Hourly Observational Data, http://www.ncdc.noaa.gov/.
11. C. A. Pope, R. T. Burnett, G. D. Thurston, M. J. Thun, E. E. Calle, and D. Krewski. Cardiovascular Mortality and Long-Term Exposure to Particulate Air Pollution: Epidemiological Evidence of General Pathophysiological Pathways of Disease. Circulation, 109(1):71-77, 2003.
12. J. B. Ruidavets, M. Cournot, S. Cassadou, M. Giroux, M. Meybeck, and J. Ferrieres. Ozone air pollution is associated with acute myocardial infarction. Circulation, 111(5):563-569, 2005.
13. M. van Heeswijk. Advances in Extreme Learning Machines. PhD thesis, Aalto University, 2015.
14. M. van Heeswijk, Y. Miche, E. Oja, and A. Lendasse. GPU-accelerated and parallelized ELM ensembles for large-scale regression. Neurocomputing, 74(16):2430-2437, 9 2011.
15. C.-M. Vong, W.-F. Ip, P.-K. Wong, and C.-C. Chiu. Predicting minority class for suspended particulate matters level by extreme learning machine. Neurocomputing, 128:136-144, 3 2014.
16. D. Voukantsis, K. Karatzas, J. Kukkonen, T. Räsänen, A. Karppinen, and M. Kolehmainen. Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki. The Science of the total environment, 409(7):1266-1276, 2011.
17. C. Zhao. Air quality forecasting using neural networks. M.Sc. thesis, 2016.