The significance of periodic parameters for ANN modeling of daily SO 2 and NOx concentrations: A case study of Belgrade, Serbia

Atmospheric Pollution Research

Volumn 10, Issue 2, 2019, Pages 621-628

  Radojević, Darinka ^a   Antanasijević, Davor ^b   Perić Grujić, Aleksandra ^b   Ristić, Mirjana ^b   Pocajt, Viktor ^b  

^a Ministry of Science and Environmental Protection   (Serbia)

^b UNIVERSITY OF BELGRADE   (Serbia)

Author keywords

Air pollutant forecasting; GRNN; Month of year; Urban air pollution; Ward neural network

Indexed keywords

EID: 85061674055     PISSN: 13091042     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apr.2018.11.004     Document Type: Article

References (26)

1. Antanasijević, D.Z., Ristić, M.Đ., Perić-Grujić, A. a., Pocajt, V.V., Forecasting human exposure to PM10 at the national level using an artificial neural network approach. J. Chemom. 27 (2013), 170–177.
2. Antanasijević, D.Z., Pocajt, V.V., Povrenović, D.S., Ristić, M.Đ., Perić-Grujić, A.A., PM 10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci. Total Environ. 443 (2013), 511–519.
3. Antanasijević, D., Pocajt, V., Perić-Grujić, A., Ristić, M., Modelling of dissolved oxygen in the Danube River using artificial neural networks and Monte Carlo Simulation uncertainty analysis. J. Hydrol. 519 (2014), 1895–1907.
4. Antanasijević, D.Z., Ristić, M.Đ., Perić-Grujić, A.A., Pocajt, V.V., Forecasting GHG emissions using an optimized artificial neural network model based on correlation and principal component analysis. Int. J. Greenh. Gas Control 20 (2014), 244–253.
5. Arhami, M., Kamali, N., Rajabi, M.M., Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations. Environ. Sci. Pollut. Res. Int. 20 (2013), 4777–4789.
6. Bianchini, M., Iorio, E. Di, Maggini, M., Mocenni, C., Pucci, A., A cyclostationary neural network model for the prediction of the NO2 concentration. ESANN'2006 Proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), 2006, 26–28.
7. Brunelli, U., Piazza, V., Pignato, L., Sorbello, F., Vitabile, S., Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo, Italy. Atmos. Environ. 41 (2007), 2967–2995.
8. Carbajal-Hernández, J.J., Sánchez-Fernández, L.P., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Assessment and prediction of air quality using fuzzy logic and autoregressive models. Atmos. Environ. 60 (2012), 37–50.
9. Gao, M., Yin, L., Ning, J., Artificial neural network model for ozone concentration estimation and Monte Carlo analysis. Atmos. Environ. 184 (2018), 129–139.
10. Gardner, M.W., Dorling, S.R., Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London. Atmos. Environ. 33 (1999), 709–719.
11. Ibarra-Berastegi, G., Elias, A., Barona, A., Saenz, J., Ezcurra, A., Diaz de Argandoña, J., From diagnosis to prognosis for forecasting air pollution using neural networks: air pollution monitoring in Bilbao. Environ. Model. Softw. 23 (2008), 622–637.
12. Juhos, I., Makra, L., Tóth, B., Forecasting of traffic origin NO and NO2 concentrations by support vector machines and neural networks using principal component analysis. Simulat. Model. Pract. Theory 16 (2008), 1488–1502.
13. Kalogirou, S.A., Artificial intelligence for the modeling and control of combustion processes: a review. Prog. Energy Combust. Sci. 29 (2003), 515–566.
14. Knežević, J., Jović, B., Marić Tanasković, L., Stanje kvaliteta vazduha u Republici Srbiji u svetlu daljih evropskih integracija, in Serbian. 45th Conference “Air Protection 2017” Proceedings, Subotica, Serbia, 2017 Available at: http://www.sepa.gov.rs/download/prezentacije/2017/SavetZastitaVazduha2017_KvVazduha.pdf.
15. Kukkonen, J., Partanen, L., Karppinen, A., Ruuskanen, J., Junninen, H., Kolehmainen, M., Niska, H., Dorling, S., Chatterton, T., Foxall, R., Cawley, G., Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki. Atmos. Environ. 37 (2003), 4539–4550.
16. Luna, A.S., Paredes, M.L.L., de Oliveira, G.C.G., Corrêa, S.M., Prediction of ozone concentration in tropospheric levels using artificial neural networks and support vector machine at Rio de Janeiro, Brazil. Atmos. Environ. 98 (2014), 98–104.
17. Olesen, H.R., The model validation exercise at mol: overview of results. Int. J. Environ. Pollut. 5 (1995), 761–784.
18. Ordieres, J.B., Vergara, E.P., Capuz, R.S., Salazar, R.E., Neural network prediction model for fine particulate matter (PM2.5) on the US–Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua). Environ. Model. Softw. 20 (2005), 547–559.
19. Raheli, B., Aalami, M.T., El-Shafie, A., Ghorbani, M.A., Deo, R.C., Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River. Environ. Earth Sci., 76, 2017, 503.
20. Rumelhart, D.E., Hinton, G., Williams, R., Learning representations by back-propagating errors. Nature 323 (1986), 534–536.
21. Singh, K.P., Gupta, S., Kumar, A., Shukla, S.P., Linear and nonlinear modeling approaches for urban air quality prediction. Sci. Total Environ. 426 (2012), 244–255.
22. Smith, B.A., Mcclendon, R.W., Hoogenboom, G., An enhanced artificial neural network for air temperature prediction. Int. J. Comput. Electr. Autom. Control Inf. Eng. 1 (2007), 2160–2165.
23. Smith, B.A., Mcclendon, R.W., Hoogenboom, G., Improving air temperature prediction with artificial neural networks. Int. J. Comput. Inf. Eng., 1, 2007, 3159.
24. Vlachogianni, A., Kassomenos, P., Karppinen, A., Karakitsios, S., Kukkonen, J., Evaluation of a multiple regression model for the forecasting of the concentrations of NOx and PM10 in Athens and Helsinki. Sci. Total Environ. 409 (2011), 1559–1571.
25. Wang, P., Liu, Y., Qin, Z., Zhang, G., A novel hybrid forecasting model for PM10 and SO2 daily concentrations. Sci. Total Environ. 505 (2015), 1202–1212.
26. Willmott, C.J., Matsuura, K., Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res. 30 (2005), 79–82.