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| Titel |
An artificial neural network predictor for tropospheric surface duct phenomena |
| VerfasserIn |
S. A. Isaakidis, I. N. Dimou, T. D. Xenos, N. A. Dris |
| 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 ; 14, no. 5 ; Nr. 14, no. 5 (2007-09-03), S.569-573 |
| Datensatznummer |
250012273
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| Publikation (Nr.) |
 copernicus.org/npg-14-569-2007.pdf |
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| Zusammenfassung |
| In this work, an artificial neural network (ANN) model is developed and used
to predict the presence of ducting phenomena for a specific time, taking
into account ground values of atmospheric pressure, relative humidity and
temperature. A feed forward backpropagation ANN is implemented, which is
trained, validated and tested using atmospheric radiosonde data from the
Helliniko airport, for the period from 1991 to 2004. The network's quality
and generality is assessed using the Area Under the Receiver Operating
Characteristics (ROC) Curves (AUC), which resulted to a mean value of about
0.86 to 0.90, depending on the observation time. In order to validate the
ANN results and to evaluate any further improvement options of the proposed
method, the problem was additionally treated using Least Squares Support
Vector Machine (LS-SVM) classifiers, trained and tested with identical data
sets for direct performance comparison with the ANN. Furthermore, time
series prediction and the effect of surface wind to the presence of
tropospheric ducts appearance are discussed. The results show that the ANN
model presented here performs efficiently and gives successful tropospheric
ducts predictions. |
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