Improving the prediction of average total ozone in column over the Iberian Peninsula using neural networks banks

Neurocomputing

Volumn 74, Issue 9, 2011, Pages 1492-1496

  Salcedo Sanz, S ^a   Camacho, J L ^b   Perez Bellido A M ^a   Ortiz Garcia, E G ^a   Portilla Figueras, A ^a   Hernandez Martin E ^c  

^a UNIVERSITY OF ALCALÁ   (Spain)

^b Terrestrial Remote Sensing Unit   (Spain)

^c UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

Author keywords

Forecasting; Neural network banks; Total ozone in column (TOC)

Indexed keywords

DATA SERIES; DIFFERENT STRUCTURE; IBERIAN PENINSULA; OZONE MEASURE; SATELLITE DATA; TOTAL OZONE; TOTAL OZONE IN COLUMN (TOC);

FORECASTING; OZONE;

NEURAL NETWORKS;

OZONE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CONCENTRATION (PARAMETERS); CONTROLLED STUDY; FRANCE; INTERMETHOD COMPARISON; PORTUGAL; PREDICTION; PRIORITY JOURNAL; SEASONAL VARIATION; SPAIN;

EID: 79953031599     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.01.003     Document Type: Article

References (25)

1. Massart B., Kvalheim O.M. Ozone forecasting from meteorological variables: part I. Predictive models by moving window and partial least squares regression. Chemometrics and Intelligent Laboratory Systems 1998, 42(1-2):179-190.
2. Massart B., Kvalheim O.M. Ozone forecasting from meteorological variables: part II. Daily maximum ground-level ozone concentration from local weather forecasts. Chemometrics and Intelligent Laboratory Systems 1998, 42(1-2):191-197.
3. Panella M., Rizzi A., Martinelli G. Refining accuracy of environmental data prediction by MoG neural networks. Neurocomputing 2003, 55(3-4):521-549.
4. Lu W.Z., Fan H.Y., Lo S.M. Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong. Neurocomputing 2003, 51:387-400.
5. Bramstedt K., Gleason J., Loyola D., Thomas W., Bracher A., Weber M., Burrows J.P. Comparison of total ozone from the satellite instruments GOME and TOMS with measurements from the Dobson network 1996-2000. Atmospheric Chemistry and Physics 2003, 3:1409-1419.
6. Latha K.M., Badarinath K.V. Impact of aerosols on total columnar ozone measurements a case study using satellite and ground-based instruments. Atmospheric Research 2003, 66(4):307-313.
7. Silva A.A. A quarter century of TOMS total column ozone measurements over Brazil. Journal of Atmospheric and Solar-Terrestrial Physics 2007, 69(12):1447-1458.
8. Savastiouk V., McElroy C.T. Brewer spectrophotometer total ozone measurements made during the 1998 middle atmosphere nitrogen trend assessment Campaign. Atmosphere-Ocean 2005, 43(4):315-324.
9. Bronnimann S., Luterbacher J., Schmutz C., Wanner H., Staehelin J. Variability of total ozone at Arosa, Switzerland, since 1931 related to atmospheric circulation indices. Geophysical Research Letters 2000, 27(15):2213-2216.
10. Steinbrecht W., Hassler B., Claude H., Winkler P., Stolarski R.S. Global distribution of total ozone and lower stratospheric temperature variations. Atmospheric Chemistry and Physics 2003, 3:1421-1438.
11. Comrie A.C. Comparing neural networks and regression models for ozone forecasting. Journal of Air & Waste Management Association 1997, 47:360-363.
12. Coman A., Ionescu A., Candau Y. Hourly ozone prediction for a 24-h horizon using neural networks. Environmental Modelling and Software 2008, 23:1407-1421.
13. Chattopadhyay G., Chattopadhyay S. Autoregressive forecast of monthly total ozone concentration: a neurocomputing approach. Computers & Geosciences 2009, 35:1925-1932.
14. Monge-Sanz B.M., Medrano-Marqués N.J. Total ozone time series analysis: a neural network model approach. Non-linear Processes in Geophysics 2004, 11:683-689.
15. Chattopadhyay S., Bandyopadhyay G. Artificial neural network with backpropagation learning to predict mean monthly total ozone in Arosa, Switzerland. International Journal of Remote Sensing 2007, 28(20):4471-4482.
16. Salcedo-Sanz S., Pérez-Bellido A.M., Ortiz-García E.G., Portilla-Figueras A., Prieto L., Correoso F. Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks. Neurocomputing 2009, 72:1336-1341.
17. Hu Y., Liu J., Li W. Resolution of overlapping spectra by curve-fitting. Analytica Chimica Acta 2005, 538:383-389.
18. Karakaplan M. Fitting lorentzian peaks with evolutionary genetic algorithm based on stochastic search procedure. Analytica Chimica Acta 2007, 587:235-239.
19. Polo-Corpa M.J., Salcedo-Sanz S., Pérez-Bellido A.M., López-Espi P., Benavente R., Pérez E. Curve fitting using heuristics and bio-inspired optimization algorithms for experimental data processing in chemistry. Chemometrics and Intelligent Laboratory Systems 2009, 96:34-42.
20. R.D. McPeters, et al., Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) Data Products User's Guide, NASA Reference Publication, 1996.
21. P.K. Bhartia, et al., The Version 8 SBUV Ozone Profile Algorithm, in: Proceedings of the Quadrennial Ozone Symposium, Kos, Greece, 2004.
22. Stolarski R.S., Douglass A.R., Steenrod S., Pawson S. Trends in stratospheric ozone: lessons learned from a 3D chemical transport model. Journal of Atmospheric Science 2006, 63(3):1028-1041.
23. Wolter K., Timlin M.S. Measuring the strength of ENSO-How does 1997/98 rank?. Weather 1998, 53:315-324.
24. United Nations Environment Programme, Environmental Effects Assessment Panel, Environmental effects of ozone depletion and its interactions with climate change: Progress Report 2005, Photochemical & Photobiological Science, 5 (13) 2006.
25. Hagan M.T., Menhaj M.B. Training feed forward network with the Marquardt algorithm. IEEE Transactions on Neural Networks 1994, 5(6):989-993.