Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions

Atmospheric Pollution Research

Volumn 7, Issue 3, 2016, Pages 557-566

  Bai, Yun ^a   Li, Yong ^b   Wang, Xiaoxue ^c   Xie, Jingjing ^a   Li, Chuan ^b  

^a ANHUI SCIENCE AND TECHNOLOGY UNIVERSITY   (China)

^b CHONGQING TECHNOLOGY AND BUSINESS UNIVERSITY   (China)

^c Nanan District Environmental Monitoring Station of Chongqing   (China)

Author keywords

Air pollutants concentrations; Back propagation neural network; Forecasting; Meteorological data; Stationary wavelet transform

Indexed keywords

EID: 84964952463     PISSN: 13091042     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apr.2016.01.004     Document Type: Article

References (47)

1. 10 forecasting using soft computing techniques. Res. J. Appl. Sci. Eng. Technol. 2014, 7:3253-3265.
2. 10 at the national level using an artificial neural network approach. J. Chemom. 2013, 27:170-177.
3. Bai Y., Wang P., Li C., Xie J., Wang Y. A multi-scale relevance vector regression approach for daily urban water demand forecasting. J. Hydrol. 2014, 517:236-245.
4. Chaudhuri S., Acharya R. Artificial neural network model to forecast the concentration of pollutants over Delhi: skill assessment of learning rules. Asian J. Water Environ. Pollut. 2012, 1:71-81.
5. 2.5 in California's San Joaquin Valley. Atmos. Environ. 2014, 92:182-190.
6. 10 concentration forecast model based on stepwise regression and wavelet analysis. Atmos. Environ. 2013, 74:346-359.
7. Chuang M.T., Zhang Y., Kang D. Application of WRF/Chem-MADRID for real-time air quality forecasting over the Southeastern United States. Atmos. Environ. 2011, 45:6241-6250.
8. 2 pollution incidents by means of Elman artificial neural networks and ARIMA models. Abstr. Appl. Analysis 2013, 4:1728-1749.
9. Daszykowski M., Serneels S., Kaczmarek K., Van Espen P., Croux C., Walczak B. TOMACT: a MATLAB toolbox for multivariate calibration techniques. Chemom. Intell. Lab. Syst. 2007, 85:269-277.
10. 2.5 analog forecast and Kalman filter post-processing for the Community Multiscale Air Quality (CMAQ) model. Atmos. Environ. 2015, 108:76-87.
11. Domańska D., Woktylak M. Application of fuzzy time series models for forecasting pollution concentrations. Expert Syst. Appl. 2012, 39:7673-7679.
12. Elangasinghe M.A., Singhal N., Dirks K.N., Salmond J.A. Development of an ANN-based air pollution forecasting system with explicit knowledge through sensitivity analysis. Atmos. Pollut. Res. 2014, 5:696-708.
13. 2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmos. Environ. 2015, 107:118-128.
14. Feng Y., Zhang W., Sun D., Zhang L. Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification. Atmos. Environ. 2011, 45:1979-1985.
15. Gantt B., Meskhidze N., Zhang Y., Xu J. The effect of marine isoprene emissions on secondary organic aerosol and ozone formation in the coastal United States. Atmos. Environ. 2010, 44:115-121.
16. Gao X.L., Hu T.J., Wang K. Research on motor vehicle exhaust pollution monitoring technology. Appl. Mech. Mater. 2014, 620:244-247.
17. He J., Yu Y., Liu N., Zhao S. Numerical model-based relationship between meteorological conditions and air quality and its implication for urban air quality management. Int. J. Environ. Pollut. 2013, 53:265-286.
18. Jian L., Zhao Y., Zhu Y.P., Zhang M., Bertolatti D. An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China. Sci. Total Environ. 2012, 426:336-345.
19. Jumah A.A. Denoising of an image using discrete stationary wavelet transform and various thresholding techniques. J. Signal Inf. Process. 2013, 4:33-41.
20. Kurt A., Oktay A.B. Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks. Expert Syst. Appl. 2010, 37:7986-7992.
21. Lalis J.T., Gerardo B.D., Byun Y. An adaptive stopping creterion for backpropagetion learning in feedforward neural network. Int. J. Multimedia Ubiquitous Eng. 2014, 9:149-156.
22. Li C., Liang M. Separation of vibration-induced signal of oil debris sensor for vibration monitoring. Smart Mater. Struct. 2011, 20:1044-1052.
23. Li C., Liang M., Wang T.Y. Criterion fusion for spectral segmentation and its application to optimal demodulation of bearing vibration signals. Mech. Syst. Signal Process. 2015, 64-65:132-148.
24. 2.5. Appl. Mech. Mater. 2014, 590:888-894.
25. 10 concentrations in Central Anatolia Region, Turkey. Atmos. Pollut. Res. 2015, 6:735-741.
26. Pai T.Y., Hanaki K., Chiou R.J. Forecasting hourly roadside particulate matter in Taipei county of Taiwan based on first-order and one-variable grey model. Clean - Soil Air Water 2013, 41:737-742.
27. Pai T.Y., Hanaki K., Su H., Yu L.F. A 24-h forecast of oxidant concentration in Tokyo using neural network and fuzzy learning approach. Clean - Soil Air Water 2013, 41:729-736.
28. 10 concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management. Environ. Sci. Pollut. Res. 2011, 18:316-327.
29. Pauzi H.M., Abdullah L. Neural network training algorithm for carbon dioxide emissions forecast: a performance comparison. Lect. Notes Electr. Eng. 2015, 315:717-726.
30. Plaia A., Salvo F.D., Ruggieri M., Agró G. A multisite-multipollutant air quality index. Atmos. Environ. 2013, 70:387-391.
31. Rumelhart D.E., Hinton G.E., Williams R.J. Learning representations by back-propagating errors. Nature 1986, 323:533-536.
32. Russo A., Lind P., Raischel F., Trigo R., Mendes M. Neural network forecast of daily pollution concentration using optimal meteorological data at synoptic and local scales. Atmos. Pollut. Res. 2015, 6. 10.5094/APR.2015.060.
33. Russo A., Soares A.O. Hybrid model for urban air pollution forecasting: a stochastic spatio-temporal approach. Math. Geosci. 2014, 46:75-93.
34. 2.5 pollution episodes in very stable nocturnal conditions and complex terrain using WRF-Chem CO tracer model. Atmos. Environ. 2011, 45:2769-2780.
35. Samia A., Kaouther N., Abdelwahed T. A hybrid ARIMA and artificial neural networks model to forecast air quality in urban areas: case of Tunisia. Adv. Mater. Res. 2012, 518-523:2969-2979.
36. Shen H.Y., Wang Z.X., Gao C.Y., Qin J., Yao F.B., Xu W. Determining the number of BP neural network hidden layer units. J. Tianjin Univ. Technol. 2008, 24:13-15.
37. Singh K.P., Gupta S., Kumar A., Shukla S.P. Linear and nonlinear modeling approaches for urban air quality prediction. Sci. Total Environ. 2012, 426:244-255.
38. Trigo R.M., Palutikof J.P. Simulation of daily temperatures for climate change scenarios over Portugal: a neural network model approach. Clim. Res. 1999, 13:45-59.
39. Urbanski S.P., Hao W.M., Nordgren B. The wildland fire emission inventory: western United States emission estimates and an evaluation of uncertainty. Atmos. Chem. Phys. 2011, 11:12973-13000.
40. Wang H.F., Xing C., Yu F. Study of the hydrological time series similarity search based on Daubechies wavelet transform. Lect. Notes Electr. Eng. 2014, 238:2051-2057.
41. 2.5 concentrations in Beijing. Atmos. Environ. 2013, 81:158-165.
42. Wang L., Zhang N., Liu Z., Sun Y., Ji D., Wang Y. The influence of climate factors, meteorological conditions, and boundary-layer structure on severe haze pollution in the Beijing-Tianjin-Hebei Region during January 2013. Adv. Meteorol. 2014, http://dx.doi.org/10.1155/2014/685971.
43. 10 in Beijing with Community Multi-scale Air Quality Modeling (CMAQ) system: emission and improvement. Geosci. Model Dev. 2014, 7:2243-2259.
44. 10 air pollution index prediction in the urban area of Wuhan, China. Environ. Eng. Sci. 2011, 28:357-363.
45. Wu W., Zha Y., Zhang J., Gao J., He J. A temperature inversion-induced air pollution process as analyzed from Mie LiDAR data. Sci. Total Environ. 2014, 479-480:102-108.
46. Yahya K., Zhang Y., Vukovich J.M. Real-time air quality forecasting over the southeastern United States using WRF/Chem-MADRID: multiple-year assessment and sensitivity studies. Atmos. Environ. 2014, 92:318-338.
47. Zhang Y., Seigneur C., Bocquet M., Mallet V., Baklanov A. Real-time air quality forecasting, part I: history, techniques, and current status. Atmos. Environ. 2012, 60:632-655.