Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations

@article{Prybutok2000ComparisonON,
  title={Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations},
  author={Victor R. Prybutok and Junsub Yi and David Mitchell},
  journal={European Journal of Operational Research},
  year={2000},
  volume={122},
  pages={31-40}
}
In an e€ort to forecast daily maximum ozone concentrations, many researchers have developed daily ozone forecasting models. However, this continuing worldwide environmental problem suggests the need for more accurate models. Development of these models is dicult because the meteorological variables and photochemical reactions involved in ozone formation are complex. In this study, a neural network model for forecasting daily maximum ozone levels is developed and compared with two conventional… CONTINUE READING
Highly Cited
This paper has 58 citations. REVIEW CITATIONS
33 Citations
24 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 33 extracted citations

58 Citations

0510'05'08'11'14'17
Citations per Year
Semantic Scholar estimates that this publication has 58 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 24 references

Ozone forecasting using empirical modeling

  • G. H. Revlett
  • Journal of Air Pollution Control Association
  • 1978
Highly Influential
10 Excerpts

Time-series analysis of Riverside, California air quality data

  • D. P. Chock, T. R. Terrell, S. B. Levitt
  • Atmospheric Environment
  • 1975
Highly Influential
6 Excerpts

An empirical model for forecasting maximum daily ozone levels in the northeastern U.S

  • G. T. Wol, P. J. Lioy
  • Journal of Air Pollution Control Association
  • 1978
Highly Influential
7 Excerpts

Arti®cial neural network models for forecasting and decision making

  • T. Hill, L. Marquez, M. O'Connor, W. Remus
  • International Journal of Forecasting
  • 1994
1 Excerpt

A neural networkbased method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain

  • M. Boznar, M. Lesjak, P. Mlakar
  • Atmospheric Environment
  • 1993
3 Excerpts

Application of the back propagation neural network algorithm with monoticity constraints for two-group classi®cation problems

  • N. P. Archer, S. Wang
  • Decision Sciences
  • 1993
1 Excerpt

Simulation of polysaccharide C nuclear magnetic resonance spectra using regression analysis and neural networks

  • J. W. Ball, P. C. Jur
  • Analytical Chemistry
  • 1993
2 Excerpts

Similar Papers

Loading similar papers…