Forecasts using neural network versus box-jenkins methodology for ambient air quality monitoring data

Journal of the Air and Waste Management Association

Volumn 50, Issue 2, 2000, Pages 219-226

  Kao, Jehng Jung ^a   Huang, Shang Shuang ^b  

^a NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^b Central Insurance Co Ltd   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

OZONE; SULFUR DIOXIDE;

AIR MONITORING; AIR POLLUTION INDICATOR; AIR QUALITY; AIR QUALITY STANDARD; AMBIENT AIR; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; INDOOR AIR POLLUTION; PRIORITY JOURNAL;

EID: 0034131752     PISSN: 10473289     EISSN: None     Source Type: Journal    
DOI: 10.1080/10473289.2000.10463997     Document Type: Article

References (21)

1. California Air Resources Board. An Evaluation of a Medical Advisory Notice to Persons with Respiratory Disease or Coronary Artery Disease; Technical Advisory Committee Report: Sacramento, CA, 1972.
2. Merz, P.H.; Painte, L.J.; Ryason, PR. “Aerometric data analysis—time-series analysis and forecast and an atmospheric smog,” Atmos. Environ. 1972, 6, 319-342.
3. Chock, D.P.; Terrell, T.R.; Levitt, S.B. “Time-series analysis of Riverside, California air quality,” Atmos. Environ. 1975, 9, 978-989.
4. Horowitz, J.; Barakat, S. “Statistical analysis of the maximum concentration of an air pollutant: effects of autocorrelation and non-stationarity,” Atmos. Environ. 1979, 13, 811-818.
5. Murray, L.C.; Farber, R.H. “Time-series analysis of an historical visibility data base,” Atmos. Environ. 1992, 10, 2299-2308.
6. McCollister, G.M. “Linear stochastic models for forecasting daily maxima and hourly concentrations of air pollutants,” Atmos. Environ. 1975, 9, 417-423.
7. Kapoor, S.G.; Terry, W.R. A Comparison of Two Automatic Systems for Building Vector Time-Series Models in Air Pollution Research. In Time-Series Analysis: Theory and Practice, vol. 7; Anderson, O.D., Ed.; Elsevier Science Publishers: Cincinnati, OH, 1985, 211-200.
8. SAS Institute Inc. SAS/ETS Software: User’s Guide: Cary, NC, 1991.
9. Nelson, M.M.; Illingworth, W.T. Neural Nets; Addison-Wesley: Reading, MA, 1991.
10. 2 concentrations in highly polluted industrial areas of complex terrain,” Atmos. Environ. 1993, 27B, 221-230.
11. Comrie, A.C. “Comparing neural networks and regression models for ozone forecasting,” J. Air & Waste Manage. Assoc. 1997, 47, 653-663.
12. Lapedes, A.; Farber, R. Nonlinear Signal Processing Using Neural Networks. Presented at IEEE Conference on Neural Information Processing Systems—Neural and Synthetic, Denver, CO, November 8-12, 1987.
13. Chakraborty, K.; Mehrotra, K.; Mohan, C.K.; Ranka, S. “Forecasting the behavior of multivariate time-series using neural networks,” Neural Networks 1992, 5, 961-970.
14. Box, G.E.P.; Jenkins, G.M. Time-Series Analysis Forecasting and Control; Holden-Day: San Francisco, CA, 1977.
15. Bowerman, B.L.; O’Connell, R.T. Forecasting and Time-series: An Applied Approach; 3rd edition; Duxbury Press: Belmont, CA, 1993.
16. Newton, H.J. TIMESLAB: A Time-Series Analysis Laboratory, Wadsworth & Brooks/Cole Publishing Company: Pacific Grove, CA, 1989.
17. Ljung, G.M.; Box, G.E.P. “On a measure of lack of fit in time models,” Biometrika 1978, 64, 517-522.
18. Cybenko, G. “Approximation by superposition of a sigmoidal function,” Math. Control Signal System 1999, 2, 303-314.
19. Weigend, A.S.; Huberman, B.A.; Rumelhart, D.E. “Predicting the future: a connectionist approach,” Intl. J. Neural Systems 1990, 1, 193-209.
20. Camp, D.; Plate, T.; Hinton, G. The XERIONNeural Network Simulator, Version 3.1; Department of Computer Science, University of Toronto: Toronto, Canada, 1993.
21. Rumelhart, D.E.; McClelland, J.L; Parallel Distributed Processing Research Group. Parallel Distributed Processing; MIT Press: Cambridge, MA, 1989.