Short-term prediction of NO2 and NOx concentrations using multilayer perceptron neural network: a case study of Tabriz, Iran

Ecological Processes

Volumn 6, Issue 1, 2017, Pages

  Rahimi, Akbar ^a  

^a UNIVERSITY OF TABRIZ   (Iran)

Author keywords

Air pollution prediction; Artificial neural network; Multilayer perceptron; NO2; NOx

Indexed keywords

AIR QUALITY; APPROXIMATION THEORY; FORECASTING; METEOROLOGY; MULTILAYER NEURAL NETWORKS; MULTILAYERS; NETWORK ARCHITECTURE; NEURAL NETWORKS; POLLUTION; STRUCTURAL OPTIMIZATION;

AIR POLLUTION PREDICTIONS; AIR QUALITY PARAMETERS; CORRELATION COEFFICIENT; METEOROLOGICAL CONDITION; METROLOGICAL PARAMETERS; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; MULTIPLE LINEAR REGRESSIONS; SCALED CONJUGATE GRADIENTS;

NITROGEN OXIDES;

ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC POLLUTION; CONCENTRATION (COMPOSITION); HEALTH IMPACT; NITROGEN DIOXIDE; NITROGEN OXIDES; PREDICTION;

EAST AZERBAIJAN; IRAN; TABRIZ;

EID: 85043828919     PISSN: None     EISSN: 21921709     Source Type: Journal    
DOI: 10.1186/s13717-016-0069-x     Document Type: Article

References (45)

1. Antanasijević DZ, Pocajt VV, Povrenović DS, Ristić MĐ, Perić-Grujić AA (2013) PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci Total Environ 443:511–519
2. Bai Y, Li Y, Wang X, Xie J, Li C (2016) Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions. Atmos Pollut Res 7:557e566
3. Bedoui S, Gomri S, Samet H, Kachouri A (2016) A prediction distribution of atmospheric pollutants using support vector machines, discriminant analysis and mapping tools (case study: Tunisia). Pollution 2(1):11–23
4. Bhaskar BV, Rajasekhar RVJ, Muthusubramaian P, Kesarkar AP (2008) Measurement and modeling of respirable particulate (PM10) and lead pollution over Madurai, India. Air Qual Atmos Health 1:45–55
5. Breuste J, Rahimi A (2015) Many public urban parks, but who profits from them? The example of Tabriz, Iran. Ecological Processes 4(6):1–15
6. Brunelli U, Piazza V, Pignato L, Vitabile S (2008) Three hours ahead prevision of SO2 pollutant concentration using an Elman neural based forecaster. Build Environ 43(3):304–314
7. Chaloulakou A, Grivas G, Spyrellis N (2003a) Neural network and multiple regression models for PM10 prediction in Athens, a comparative assessment. J Air Waste Manage Assoc 53:1183–1190.
8. Chaloulakou A, Saisana M, Spyrellis N (2003b) Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens. Sci Total Environ 313:1–13
9. Chelani AB, Chalapati RCV, Hasan MZ (2002) Prediction of Sulphur Dioxide Concentration Using Artificial Neural Networks. Environ Model Softw 17(2):159–166.
10. Collet RS, Oduyemi K (1997) Air quality modelling: a technical review of mathematical approaches. Meteorol Appl 4:235–246
11. Collett RS, Oduyemi K (1997) Air quality modeling: a technical review of mathematical approaches. Meteorol Appl 4:235–46
12. Elangasinghe MA, Singhal N, Dirks KN, Salmond JA, Samarasinghe S (2014) Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering. Atmos Environ 94:106e116
13. Garcia Nieto PJ, Alvarez Antَn JC (2014) Nonlinear air quality modeling using multivariate adaptive regression splines in Gijón urban area (Northern Spain) at local scale. Appl Math Comput 235:50–65
14. Gardner MW, Dorling SR (1998) Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos Environ 32(14/15):2627–2636
15. Gardner MW, Dorling SR (1999) Neural network modeling and prediction of hourly NOx and NO2 concentrations in urban air in London. Atmos Environ 33:709–719
16. Gardner MW, Dorling SR (2000) Meteorologically adjusted trends in UK daily maximum surface ozone concentrations. Atmos Environ 34(2):171–176
17. Garson GD (1991) Interpreting neural network connection weights, Artif, Intell. Export 6:47–51
18. Grivas G, Chaloulakou A (2006) Artificial neural network models for prediction of PM10 hourly concentrations, in the greater area of Athens, Greece. Atmos Environ 40:1216–1229
19. Gürmen NM, Fogler HS (2006) Modeling the accumulation and depletion of smog in Los Angeles. Retrieve July, from: http://www.umich.edu/~essen/html/web_mod/la_basin/solution.htm
20. Hong WC, Dong Y, Zheng F, Lai CY (2011) Forecasting urban traffic flow by SVR with continuous ACO. Appl Math Model 35(3):1282–91
21. Jiang D, Zhanga Y, Hua X, Zenga Y, Tanb J, Shao D (2004) Progress in developing an ANN model for air pollution index forecast. Atmos Environ 38:7055–7064
22. Kesarkar AP, Dalvi M, Kaginlkar A, Ojha A (2007) Coupling of the weather research and forecasting model with AERMOD for pollutant dispersion modeling. A case study for PM10 dispersion over Pune, India. Atmos Environ 41:1976–88
23. Khani K (2016) Air pollution in Iran. https://themediaexpress.com/2016/05/16/air-pollution-in-iran/
24. Kolmogorov AN (1957) On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition. Doklady Akademii Nauk SSR 114:953–956
25. Mojtabazadeh H (2005) The role of industries in air pollution and guidelines: a case study of the city of Tabriz. Sustainable Development and Planning II 1:735–741
26. Niska H, Hiltunen T, Karppinen A, Ruuskanen J, Kolehmainen M (2004) Evolving the neural network model for forecasting air pollution time series. Eng Appl Artif Intel 17:159–167
27. Olden JD, Jackson DA (2002) Illuminating the black box: a randomization approach for understanding variable contributions in artificial neural networks. Ecol, Modell 154:135–150
28. Palani S, Liong SY, Tkalich P (2008) An ANN application for water quality forecasting. Mar Pollut Bull 56:1586–1597
29. Pires JCM, Martins FG, Sousa SIV, Alvim-Ferraz MCM, Pereira MC (2008a) Prediction of the daily mean PM10 concentrations using linear models. Am J Environ Sci 4(5):445–453
30. Pires J, Martins F, Sousa S, Alvim-Ferraz M, Pereira M (2008b) Selection and validation of parameters in multiple linear and principal component regressions. Environ Model Softw 23(1):50–55.
31. Rahimi A (2016) A methodological approach to urban land use change modeling using infill development pattern—a case study in Tabriz, Iran. Ecol Process 5(1):1–15
32. Sadr Mousavi MS, Rahimi A (2008) The application of artificial neural networks in prediction of CO concentration: a case study of Tabriz. Iran J Nat Resour 61(3):681–91
33. Sadr Mousavi MS, Rahimi A (2010) Comparison of the results of multilayer perceptron neural networks and multiple linear regressions for prediction of ozone concentration in Tabriz City. Physical Geography Research Quarterly 42(71):65–72
34. Sfetsos A, Vlachogiannis D (2010) A new approach to discovering the causal relationship between meteorological patterns and PM10 exceedances. International Conference on Nucleation and Atmospheric Aerosols (Part 1) ICNAA 2009. Atmos Res 98:500–11
35. Shi JP, Harrison RM (1997) Regression modelling of hourly NOx and NO2 concentrations in urban air in London. Atmos Environ 31(24):4081–4094
36. Singh KP, Basant N, Malik A, Jain G (2010) Modeling the performance of “up-flow anaerobic sludge blanket” reactor based wastewater treatment plant using linear and nonlinear approaches—a case study. Anal Chim Acta 658:1–11
37. Singh KP, Gupta S, Kumar A, Shukla SP (2012) Linear and nonlinear modeling approaches for urban air quality prediction. Sci Total Environ 426:244–255
38. Vakil-Baghmisheh MT, Pavešic N (2003) A fast simplified fuzzy ARTMAP network. Neural Process Lett 17:273
39. Voukantsis D, Karatzas K, Kukkonen J, Räsänen T, Karppinen A, Kolehmainen M (2011) 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. Sci Total Environ 409:1266–76
40. Wang P, Liu Y, Qin Z, Zhang G (2015) A novel hybrid forecasting model for PM10 and SO2 daily concentrations. Sci Total Environ 505:1202–1212
41. WHO (World Health Organization) (2014) 7 million premature deaths annually linked to air pollution. http://www.who.int/mediacentre/news/releases/2014/air-pollution/en/
42. Xie Y, Zhao L, Xue J, Hu Q, Xu X, Wang H (2016) A cooperative reduction model for regional air pollution control in China that considers adverse health effects and pollutant reduction costs. Sci Total Environ 573:458–469
43. Zhang Y, Seigneur C, Bocquet M, Mallet V, Baklanov A (2012) Real-time air quality forecasting, part I: history, techniques, and current status. Atmos Environ 60:632e655
44. Ziomass IC, Melas D, Zerefos CS, Bais AF (1995) Forecasting peak pollutant levels from meteorological variables. Atmos Environ 29(24):3703–3711
45. Zupan J, Gasteiger J (1999) Neural network in chemistry and drug design. Wiley- VCH, Federal Republic Germany