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| Titel |
Total ozone time series analysis: a neural network model approach |
| VerfasserIn |
B. M. Monge Sanz, N. J. Medrano Marqués |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 11, no. 5/6 ; Nr. 11, no. 5/6 (2004-12-16), S.683-689 |
| Datensatznummer |
250008996
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| Publikation (Nr.) |
 copernicus.org/npg-11-683-2004.pdf |
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| Zusammenfassung |
| This work is focused on the application of neural network based models to
the analysis of total ozone (TO) time series. Processes that affect total
ozone are extremely non linear, especially at the considered European
mid-latitudes. Artificial neural networks (ANNs) are intrinsically
non-linear systems, hence they are expected to cope with TO series better
than classical statistics do. Moreover, neural networks do not assume the
stationarity of the data series so they are also able to follow
time-changing situations among the implicated variables. These two features
turn NNs into a promising tool to catch the interactions between atmospheric
variables, and therefore to extract as much information as possible from the
available data in order to make, for example, time series reconstructions or
future predictions. Models based on NNs have also proved to be very suitable
for the treatment of missing values within the data series. In this paper we
present several models based on neural networks to fill the missing periods
of data within a total ozone time series, and models able to reconstruct the
data series. The results released by the ANNs have been compared with those
obtained by using classical statistics methods, and better accuracy has been
achieved with the non linear ANNs techniques. Different network structures
and training strategies have been tested depending on the specific task to
be accomplished. |
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