Abstract
One of the main problems that arises when dealing with time series is the existence of missing values which have to be completed previously to every statistical treatment. Here we present several models based on neural networks (NNs) to fill the missing periods of data within a total ozone (TO) time series. These non linear models have been compared with linear techniques and better results are obtained by using the non linear ones. A neural network scheme suitable for TO monthly values prediction is also presented.
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References
Madronich, S.: UV radiation in the natural and perturbed atmosphere. In: M. Tevini (ed.): UV-B Radiation and Ozone Depletion. A. F. Lewis, New York (1993) 17–69
Casale G. R., Meloni, D., Miano, S., Palmieri, S., Siani, A. M., Cappellani, F.: Solar UV-B irradiance and total ozone in Italy: Fluctuations and trends. J. Geophys. Res., 105 (2000) 4895–4901
Hansen, B. K.: State of the art of neural networks in meteorology. Mid-term paper for a Neural Networks course at the Technical University of Nova Scotia (1997)
Levenberg, K.: A method for the solution of certain problems in least squares. STAM J. Numer. Anal. 16 (1944) 588–604
Marquardt, D.: An algorithm for least squares estimation of nonlinear parameters. SLAM J. Appl. Math. 11 (1963) 431–441
Widrow, B., Winter, R.: Neural nets for adaptive filtering and adaptive pattern recognition. IEEE Computer, March (1988) 25–39
Brönnimann, S., Luterbacher J., Schmutz C, Wanner H., Staehelin J.: Variability of total ozone at Arosa, since 1931 related to atmospheric circulation indices, Geophys. Res. Lett., 27 (2000) 2213–2216
Staehelin, J., Mäder, J., Weiss, A. K., Appenzeller, C: Long-term ozone trends in Northern mid-latitudes with special emphasis on the contribution of changes in dynamics. Physics and Chemistry of the Earth 27 (2002) 461–469
Trigo, R.M, Palutikof, J.P.: Simulation of daily temperatures for climate change scenarios over Portugal: a neural network approach. Climate Research, vol. 13 (1999) 45–59
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© 2003 Springer-Verlag Berlin Heidelberg
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Monge-Sanz, B., Medrano-Marqués, N. (2003). Artificial Neural Networks Applications for Total Ozone Time Series. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_102
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DOI: https://doi.org/10.1007/3-540-44869-1_102
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