Experimental and artificial neural network approach for forecasting of traffic air pollution in urban areas: The case of subotica

Thermal Science

Volumn 14, Issue SUPPL.1, 2010, Pages

  Vujić, Bogdana B ^a   Vukmirović, Srdan M ^b   Vujić, Goran V ^b   Jovičić, Nebojša M ^c   Jovičić, Gordana R ^c   Babić, Milun J ^c  

^a Provincial Secretariat for Environmental Protection and Sustainable Development   (Serbia)

^b UNIVERSITY OF NOVI SAD   (Serbia)

^c UNIVERSITY OF KRAGUJEVAC   (Serbia)

Author keywords

Artificial neural network; Forecasting PM10 concentrations; Meteorological parameters; PM10

Indexed keywords

AIR QUALITY; FOG; METEOROLOGY; URBAN PLANNING;

AMBIENT AIR; ARTIFICIAL NEURAL NETWORK APPROACH; EFFECTIVE MEASURES; FORECASTING PM10 CONCENTRATION; GASEOUS POLLUTANTS; METEOROLOGICAL PARAMETERS; PARTICLES CONCENTRATION; PM10; PM10 CONCENTRATION; URBAN AREAS;

NEURAL NETWORKS;

EID: 84860672721     PISSN: 03549836     EISSN: None     Source Type: Journal    
DOI: 10.2298/tsci100507032v     Document Type: Article

References (17)

1. Zickus, M., Greig, A., J., Niranjan, M., Comparison of Four Machine Learning Methods for Predicting PM10 Concentrations in Helsinki, Finland, Water, Air, and Soil Pollution: focus 2: pp. 717-729, 2002
2. ***, Directive 2008/50/EC of the European Parliament and of the Council 0f 21 May 2008 on Ambient Air Quality and Cleaner Air for Europe, Official Journal of the European Communities, No. L 152/1. EC, 2008
3. ***, Council Decision 97/101/EC of 27 January 1997 Establishing a Reciprocal Exchange of Information and Data from Networks and Individual Stations Measuring Ambient Air Pollution within the Member States, Official Journal of the European Communities, No. L 35/14. EC, 1997
4. Boy, M., Kulmala, M., Influence of Spectral Solar Irradiance on the Formation of New Partiles in the Continental Boundary Layer, Atmos. Chem. Phys. Discuss., 2 (2002), 5, pp. 1317-1350
5. 2, CO in the Urban Area of Palermo, Italy, Atmospheric Environment, 41 (2007), 14, pp. 2967-2995
6. ***, The Mathworks, http://www.mathworks.com/products/ (15.07.2009)
7. Papanastasiou, D. K., Melas, D., Kioutsioukis, I., Development and Assessment of Neural Network and Multiple Regression Models in Order to Predict PM10 Levels in a Medium-Sized Mediterranean City, Water Air Soil Pollution, 182 (2007), 1-4, pp. 325-334
8. Asha, B. C., Devotta, S., Air Quality Forecasting Using a Hybrid Autoregressive and Non-linear Model, Atmospheric Environment, 40 (2005), 10, pp. 1774-1780
9. Luis, A. D-R., et al., A Hybrid ARIMA and Artificial Neural Networks Model to Forecast Particulate Matter in Urban Areas: The Case of Temuco, Chile Atmospheric Environment, 42 (2008), 35, pp. 8331-8340
10. Goyal, P., Chan, T., Neeru, J., Statistical Models for the Prediction of Respirable Suspended Particulate Matter in Urban Cities, Atmospheric Environment, 40 (2006) 11, pp. 2068 - 2077
11. Casseli, M., Trizio, L., de Gennaro, G., Ielpo, P., A Simple Feed-forward Neural Network for the PM10 Forecasting: Comparison with a Radial Basis Function Network and a Multivariate Linear Regression Model, Water Air Soil Pollutant, 201 (2008), 1-4, pp. 365-377
12. Perez, P., Reyes, J., An Integrated Neural Network Model for PM10 Forecasting, Atmospheric Environment, 40 (2006), 16, pp. 2845-2851
13. Kolehmainen, M., Martikainen, H., Ruuskanen, J., Neural Networks and Periodic Components Used in Air Quality Forecasting, Atmospheric Environment, 35 (2001), 5, pp. 815-825
14. Hubbard, M., Cobourn, G., Development of a Regression Model to Forecast Ground-Level Ozone Concentration in Louisville, KY, Atmospheric Environment, 32 (1998) 14-15, pp. 2637-2647
15. Gardner, W., Dorling, R, Statistical Surface Ozone Models: An Improved Methodology to Account for Non-Linear Behavior, Atmospheric Environment, 34 (2000), 1, pp. 21-34
16. Zang, P., Qi, M., Neural Network Forecasting for Seasonal and Trend Time Series, European Journal Research, 160 (2005), 2, pp. 501-514.
17. Corrani, G., Air Quality Prediction in Milan: Neural Network, Pruned Neural Networks and Lazy Learning, Ecological Modeling, 185 (2005), 2-4, pp. 513-529