Air quality prediction using optimal neural networks with stochastic variables

Atmospheric Environment

Volumn 79, Issue , 2013, Pages 822-830

  Russo, Ana ^a   Raischel, Frank ^a   Lind, Pedro G ^a,b  

^a UNIVERSITY OF LISBON   (Portugal)

^b UNIVERSITY OF OLDENBURG   (Germany)

Author keywords

Environmental research; Neural networks; Pollutants; Stochastic systems

Indexed keywords

AIR QUALITY FORECASTS; AIR QUALITY PREDICTION; ARTIFICIAL NEURAL NETWORK MODELS; ENVIRONMENTAL RESEARCHES; NEURAL NETWORK MODEL; OPTIMAL NEURAL NETWORK; POLLUTANTS; STOCHASTIC VARIABLE;

AIR QUALITY; NEURAL NETWORKS; OPTIMIZATION; STOCHASTIC SYSTEMS;

WEATHER FORECASTING;

AIR QUALITY; DATA ACQUISITION; GEOPHYSICAL SURVEY; MULTIVARIATE ANALYSIS; NUMERICAL MODEL; OPTIMIZATION; STOCHASTICITY; WEATHER FORECASTING;

AIR QUALITY; ARTICLE; ARTIFICIAL NEURAL NETWORK; DATA ANALYSIS; FORECASTING; METHODOLOGY; PREDICTION; PRIORITY JOURNAL; SPECTROMETRY; STOCHASTIC MODEL; WEATHER;

EID: 84882768416     PISSN: 13522310     EISSN: 18732844     Source Type: Journal    
DOI: 10.1016/j.atmosenv.2013.07.072     Document Type: Article

References (46)

1. 2 levels in the Bilbao area. Environmental Modelling & Software 2006, 21:430446.
2. Boettcher F., Peinke J., Kleinhans D., Friedrich R., Lind P.G., Haase M. Reconstruction of complex dynamical systems affected by strong measurement noise. Physical Review Letters 2006, 97:090603.
3. Carvalho J., Raischel F., Haase M., Lind P.G. Evaluating strong measurement noise in data series with simulated annealing method. Journal of Physics 2011, 285:012007.
4. Cobourn W., Dolcine L., French M., Hubbard M. Acomparison of nonlinear regression and neural network models for ground-level ozone forecasting. Journal of the Air & Waste Management Association 2000, 50:19992009.
5. 3 and PM10 levels at a rural mid-latitude site. Atmospheric Chemistry and Physics 2009, 9:26952714.
6. Dias D., Tchepel O., Carvalho A., Miranda A.I., Borrego C. Particulate matter and health risk under a changing climate: assessment for Portugal. Scientific World Journal 2012, 2012:409546.
7. Dutot A.L., Rynkiewicz J., Steiner F.E., Rude J. A24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions. Environmental Modelling and Software 2007, 22:12611269.
8. Friedrich R., Peinke J. Description of a turbulent cascade by a Fokker-Planck equation. Physical Review Letters 1997, 78:863.
9. Friedrich R., Peinke J., Renner C. How to quantify deterministic and random influences on the statistics of the foreign exchange market. Physical Review Letters 2000, 84:5224.
10. Friedrich R., Peinke J., Sahimi M., Tabar M.R.R. Approaching complexity by stochastic methods: from biological systems to turbulence. Physics Reports 2011, 506:87.
11. Gardiner C.W. Handbook of Stochastic Methods 1997, Springer, Germany.
12. Gardner M.W., Dorling S.R. Artificial neural networks (the multilayer perceptron). Atmospheric Environment 1998, 32:2627-2636.
13. Gardner M., Dorling S. Statistical surface ozone models: an improved methodology to account for non-linear behaviour. Atmospheric Environment 2000, 34:2134.
14. Ghasemi F., Sahimi M., Peinke J., Friedrich R., Jafari G.R., Tabar M.R.R.m Markov analysis and Kramers-Moyal expansion of nonstationary stochastic processes with application to the fluctuations in the oil price. Physical Review E 2007, 75:060102.
15. Gradis̃ek J., Friedrich R., Govekar E., Grabec I. Examples of analysis of stochastic processes based on time series data. Meccanica 2003, 38:33-42.
16. Haykin S. Neural Networks - a Comprehensive Foundation 1999, MacMillan (College Publishing Company, New York. 2 ed.
17. Hooyberghs J., Mensink C., Dumont G., Fierens F., Brasseur O. Aneural network forecast for daily average PM10 concentrations in Belgium. Atmospheric Environment 2005, 39:32793289.
18. Kleinhans D., Friedrich R., Nawroth A., Peinke J. An iterative procedure for the estimation of drift and diffusion coefficients of Langevin processes. Physical Letters A 2005, 346:42-46.
19. Kolehmainen M., Martikainen H., Ruuskanen J. Neural networks and periodic components used in air quality forecasting. Atmospheric Environment 2001, 35:815-825.
20. 2 and PM10 concentrations, compared with a deterministic modeling system and measurements in central Helsinki. Atmospheric Environment 2003, 37:45394550.
21. Lade S.J. Finite sampling interval effects in KramersMoyal analysis. Physical Letters A 2009, 373:3705-3709.
22. Lal B., Tripathy S.S. Prediction of dust concentration in open cast coal mine using artificial neural network. Atmospheric Pollution Research 2012, 3:211-218.
23. Lind P.G., Mora A., Gallas J.A.C., Haase M. Reducing stochasticity in the North Atlantic oscillation with coupled Langevin equations. Physical Review E 2005, 72:056706.
24. Lind P.G., Mora A., Haase M., Gallas J.A.C. Minimizing stochasticity in the NAO index. International Journal of Bifurcation and Chaos 2007, 17(10):3461-3466.
25. Lind P.G., Haase M., Boettcher F., Peinke J., Kleinhans D., Friedrich R. Extracting strong measurement noise from stochastic time series: applications to empirical data. Physical Review E 2010, 81:041125.
26. Luecken D.J., Hutzell W.T., Gipson G.L. Development and analysis of air quality modeling simulations for hazardous air pollutants. Atmospheric Environment 2006, 40:5087-5096.
27. Middleton D. Anew model to forecast urban air quality: BOXURB. Environmental Monitoring and Assessment 1997, 52:315335.
28. 10 concentrations using artificial neural networks. International Journal of Environmental Research 2012, 6:277-284.
29. 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 and Soil Pollution 2007, 182:325-334.
30. Pearson K. On lines and planes of closest fit to systems of points in space. Philosophical Magazine 1901, 2:559.
31. Perez P. Prediction of sulfur dioxide concentrations at a site near downtown Santiago, Chile. Atmospheric Environment 2001, 35:49294935.
32. Perez P., Trier A., Reyes J. Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile. Atmospheric Environment 2000, 34:11891196.
33. Press W.H., Flannery B.P., Teukolsky S.A., Vetterling W.T. Numerical Recipes 1992, Cambridge University Press, Cambridge.
34. Raischel F., Russo A., Haase M., Kleinhans D., Lind P.G. Searching for optimal variables in real multivariate stochastic data. Physics Letters A 2012, 376:2081.
35. Raischel F., Scholz T., Lopes V.V., Lind P.G. Uncovering Wind Turbine Properties through Two-dimensional Stochastic Modeling of Wind Dynamics 2013, Submitted 2013.
36. Reich S., Gomez D., Dawidowski L. Artificial neural network for the identification of unknown air pollution sources. Atmospheric Environment 1999, 33:30453052.
37. Risken H. The Fokker-Planck Equation 1984, Springer, Heidelberg.
38. 2 concentrations in urban air in London. Atmospheric Environment 1999, 31(24):40814094.
39. Sokhi R.S., San José R., Kitwiroon N., Fragkou E., Pérez J.L., Middleton D.R. Prediction of ozone levels in London using the MM5CMAQ modelling system. Environmental Modelling & Software 2006, 21:566576.
40. Trigo R., DaCamara C. Circulation weather types and their impact on the precipitation regime in Portugal. International Journal of Climatology 2000, 20:15591581.
41. Trigo R., Palutikof J. Simulation of daily temperatures for climate change scenarios over Portugal: a neural network model approach. Climate Research 1999, 13:4559.
42. van Mourik A.M., Daffertshofer A., Beek P.J. Deterministic and stochastic features of rhythmic human movement. Biological Cybernetics 2006, 94:233-244.
43. Vasconcelos V., Raischel F., Haase M., Peinke J., Wächter M., Lind P.G., Kleinhans D. Principal axes of stochastic motion. Physical Review E 2011, 84:031103.
44. Weisberg S. Applied Linear Regression 1985, John Wiley & Sons, New York.
45. Wilks D. Statistical Methods in the Atmospheric Sciences 2006, No. 59 in International Geophysics, Academic Press. second ed.
46. Yi J., Prybutok V.R. Aneural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. Environmental Pollution 2002, 92(3):349357.