Neural network modelling and prediction of hourly NO(x) and NO2 concentrations in urban air in London

Atmospheric Environment

Volumn 33, Issue 5, 1999, Pages 709-719

  Gardner, M W ^a   Dorling, S R ^a  

^a UNIVERSITY OF EAST ANGLIA   (United Kingdom)

Author keywords

Air quality modelling; Artificial neural network; Multilayer perceptron; Nitrogen oxides; Primary pollutant

Indexed keywords

PRIMARY POLLUTANTS;

AIR QUALITY; MATHEMATICAL MODELS; METEOROLOGY; MULTILAYER NEURAL NETWORKS; NITROGEN OXIDES;

AIR POLLUTION;

NITROGEN OXIDE;

ATMOSPHERIC POLLUTION; NITRATE; URBAN ATMOSPHERE;

AIR POLLUTANT; AIR QUALITY; ARTICLE; ARTIFICIAL NEURAL NETWORK; MATHEMATICAL MODEL; PRIORITY JOURNAL; UNITED KINGDOM; URBAN AREA;

UNITED KINGDOM;

EID: 0033081112     PISSN: 13522310     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1352-2310(98)00230-1     Document Type: Article

References (19)

1. 2 smog episode in the U.K. Atmospheric Environment 28 (3), 461-475.
2. 2 concentrations in highly polluted industrial areas of complex terrain. Atmospheric Environment 27B (2), 221-230.
3. Collet, R.S., Oduyemi, K., 1997. Air quality modelling: A technical review of mathematical approaches. Meteorological Applications 4, 235-246.
4. Comrie, A.C., 1997. Comparing neural network and regression models for ozone forecasting. Journal of the Air and Waste Management Association 47, 653-663.
5. Derwent, R.G., Middleton, D.R., Field, R.A., Goldstone, M.E., Lester, J.N., Perry, R., 1995. Analysis and interpretation of air quality data from an urban roadside location in central london over the period from July 1991 to July 1992. Atmospheric Environment 29 (8), 923-946.
6. Gardner, M.W., Dorling, S.R., 1998. Artificial neural networks (the multilayer perceptron) - a review of applications in the atmospheric sciences, Atmospheric Environment 32 (14/15), 2627-2636.
7. Hornik, K., Stinchcoombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2, 359-366.
8. Jain, A.K., Mao, J., Mohiuddin, K.M., 1996. Artificial neural networks - a tutorial. Computer, 31-44.
9. Moller, M.F., 1991. A scaled conjugate gradient algorithm for fast supervised learning. IEEE Transactions on Systems. Man and Cybernetics 21, 272-280.
10. Moller, M.F., 1993. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6, 525-533.
11. Rao, S.T., Zurbenko, I.G., Neagu, R., Porter, P.S., Ku, J.Y., Henry, R.F., 1997. Space and time scales in ambient ozone data. Bulletin of the American Meteorological Society, 78 (10), 2153-2166.
12. Sarle, W.S., 1995. Stopped training and other remedies for over-fitting. Proceedings of the 27th Symposium on the Interface. Available via ftp: //ftp.sas.com/pub/neural/inter95.ps.Z.
13. 2 concentrations in urban air in London. Atmospheric Environment 31 (24), 4081-4094.
14. Strachan, I., Stedman, J.R., 1997. The development of a neural network model for forecasting UK air quality, Technical Report, AEAT/2346/20008001/005, Culham: AEA Technology, National Environmental Technology Centre.
15. Vukovich, F.M., 1997. Time scales of surface ozone variations in the regional, non-urban environment. Atmospheric EnvironMent 31 (10), 1513-1530.
16. Wilks, D.S., 1995. Statistical Methods in the Atmospheric Sciences: An Introduction, Academic Press, International Geophysics Series, San Diego, CA.
17. Willmott, C.J., 1982. Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society 63 (11), 1309-1313.
18. Willmott, C.J., Ackleson, S.G., Davis, R.E., Feddema, J.J., Klink, K.M., Legates, D.R., O'Donnell, J., Rowe, C.M., 1985. Statistics for the evaluation and comparison of models. Journal of Geophysical Research 90 (C5), 8995-9005.
19. Ziomass, I.C., Melas, D., Zerefos, C.S., Bais, A.F., 1995. Forecasting peak pollutant levels from meteorological variables, Atmospheric Environment 29 (24), 3703-3711.