ACPD Atmospheric Chemistry and Physics Discussions ACPD Atmos. Chem. Phys. Discuss. 1680-7375 Göttingen, Germany 10.5194/acpd-7-5739-2007 Use of neural networks for tropospheric ozone time series approximation and forecasting – a review Argiriou A. A. 1 Laboratory of Atmospheric Physics, Dept. of Physics, University of Patras, Patras, Greece 27 04 2007 7 2 5739 5767 Copyright: © 2007 A. A. Argiriou 2007 This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Generic License. To view a copy of this licence, visit https://creativecommons.org/licenses/by-nc-sa/2.5/ This article is available from https://acp.copernicus.org/preprints/7/5739/2007/acpd-7-5739-2007.html The full text article is available as a PDF file from https://acp.copernicus.org/preprints/7/5739/2007/acpd-7-5739-2007.pdf

The use of artificial neural networks in atmospheric science expands constantly. During the last years, many papers were published dealing with air pollution modeling. A number of papers deals with the time series approximation and forecasting of tropospheric ozone concentration. Neural networks have been found to outperform other statistical techniques like multiple regression etc. This paper reviews and discusses some practical aspects of the proposed neural network models applied to ozone concentration approximation and forecasting.

 References Abdul-Wahab, S A. and Al-Alawi, S M.: Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks, Environmental Modelling and Software, 17, 219–228, 2002. Anderson, J A. and Rosenfeld, E.: Neurocomputing, Foundations of Research, MIT Press, Cambridge, MA, 1988. Balaguer-Ballester, E., i~Valls, J. L., Carrasco-Rodriguez, G C., Soria-Olivas, E., and del Valle-Tascon, S.: Effective 1-day ahead prediction of hourly surface ozone concentrations in eastern Spain using linear models and neural networks, Ecological Modelling, 156, 27–41, 2002. Cannon, A J. and Lord, E R.: Forecasting summertime surface-level ozone concentrations in the Lower Fraser valley of British Columbia: An ensemble neural network approach, J. Air Waste Manage. Assoc., 50, 322–339, 2000. Chaloulakou, A., Saisana, M., and Spyrellis, N.: Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens, Sci. Tot. Environ., 29, 555–562, 2003. Cobourn, W G., Dolcine, L., French, M., and Hubbard, M C.: A comparison of nonlinear regression and neural network models for ground-level ozone forecasting, J. Air Waste Manage. Assoc., 50, 1999–2009, 2000. Comrie, A C.: Comparing neural networks and regression models for ozone forecasting, J. Air Waste Manage. Assoc., 47, 653–663, 1997. Cybenko, G.: Approximations by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems, 2, 303–314, 1989. Elkamel, A., Abdul-Wahab, S., Bouhamra, W., and Alper, E.: Measurement and prediction of ozone levels around a heavily industrialized area: a neural network approach, Adv. Environ. Res., 5, 47–59, 2001. Gardner, M. and Dorling, S.: Artificial neural network-derived trends in daily maximum surface ozone concentrations, J. Air Waste Manage. Assoc., 51, 1202–1210, 2001. Gardner, M W. and Dorling, S R.: Artificial neural networks (The multilayer perceptron) - A review of applications in the atmospheric sciences, Atmos. Environ., 32, 2627–2636, 1998. Gómez-Sanchis, J., Mart\'in-Guerro, J D., Soria-Olivas, E., Vila-Francés, J., Carrasco, J L., and del Valle-Tascón, S.: Neural networks for analysing the relevance of input variables in the prediction of tropospheric ozone concentration, Atmos. Environ., 40, 6173–6180, 2006. Guardani, R., Nascimento, C. A O., Guardani, M. L G., Martins, M. H. R B., and Romano, J.: Study of atmospheric ozone formation by means of a neural network-based model, J. Air Waste Manage. Assoc., 49, 316–323, 1999. Hadjiiski, L. and Hopke, P.: Application of artificial neural networks to modeling and prediction of ambient ozone concentrations, J. Air Waste Manage. Assoc., 50, 894–901, 2000. Heo, J S. and Kim, D S.: A new method of ozone forecasting using fuzzy expert and neural network systems, Sci. Tot. Environ., 325, 221–237, 2004. Kartalopoulos, S V.: Understanding Neural Networks and Fuzzy Logic – Basic Concepts and Application, IEEE Press, New York, 1996. Nunnari, G., Nucifora, A. F M., and Randieri, C.: The application of neural techniques to the modelling of time-series of atmospheric pollution data, Ecological Modelling, 111, 187–205, 1998. Pastor-Barcénas, O., Soria-Olivas, E., Martin-Guerro, J D., Camps-Valls, G., Carrasco-Rodr\'iguez, J L., and del Valle-Tascón, S.: Unbiased sensitivity analysis and pruning techniques in neural networks for surface ozone modelling, Ecological Modelling, 182, 149–158, 2005. Prybutok, V R., Yi, J., and Mitchell, D.: Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations, European Journal of Operational Research, 122, 31–40, 2000. Robeson, S M. and Steyn, D.: Evaluation and comparison of statistical forecast models for daily maximum ozone concentrations, Atmos. Environ., 24B, 303–312, 1990. Ruiz-Suárez, J C., Mayora-Ibarra, O., Torres-Jiménez, J., and Ruiz-Suárez, L G.: Short-term ozone forecasting by artificial neural networks, Advances in Engineering Software, 23, 143–149, 1995. Seinfeld, J H. and Pandis, S N.: Atmospheric Chemistry and Physics, Wiley, New York, 2006. Soja, G. and Soja, A.-M.: Ozone indices based on simple meteorological parameters: potentials and limitations of regression and neural network models, Atmos. Environ., 33, 4299–4307, 1999. Sousa, S. I V., Martins, F G., Alvim-Ferraz, M. C M., and Pereira, M C.: Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations, Environmental Modelling Software, 22, 97–103, 2007. Spellman, G.: An application of artificial neural networks to the prediction of surface ozone concentrations in the United Kingdom, Applied Geography, 19, 123–136, 1999. Viotti, P., Liuti, G., and Di-Genova, P.: Atmospheric urban pollution: applications of an artificial neural network (ANN) to the city of Perugia, Ecological Modelling, 148, 47–59, 2002. Wang, W., Lu, W., Wang, X., and Leung, A. Y T.: Prediction of maximum daily ozone level using combined neural network and statistical characteristics, Environment International, 29, 555–562, 2003. Willmott, C J.: On the validation of models, Physical Geography, 2, 184–194, 1981. Willmott, C J., Ackelson, S G., Davis, R E., Feddema, J J., Klink, K M., Legates, D R., O'Donnell, J., and Rowe, C M.: Statistics for the evaluation and comparison of models, J. Geophys. Res., 90(C5), 8995–9005, 1985. Yi, J. and Prybutok, V R.: A neural network forecasting for prediction of daily maximum ozone concentration in an industrialized urban area, Environ. Pollution, 92, 349–357, 1996.