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| Titel |
Nonlinear chaotic model for predicting storm surges |
| VerfasserIn |
M. Siek, D. P. Solomatine |
| 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 ; 17, no. 5 ; Nr. 17, no. 5 (2010-09-06), S.405-420 |
| Datensatznummer |
250013721
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| Publikation (Nr.) |
 copernicus.org/npg-17-405-2010.pdf |
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| Zusammenfassung |
| This paper addresses the use of the methods of nonlinear dynamics and chaos
theory for building a predictive chaotic model from time series. The chaotic
model predictions are made by the adaptive local models based on the
dynamical neighbors found in the reconstructed phase space of the
observables. We implemented the univariate and multivariate chaotic models
with direct and multi-steps prediction techniques and optimized these models
using an exhaustive search method. The built models were tested for
predicting storm surge dynamics for different stormy conditions in the North
Sea, and are compared to neural network models. The results show that the
chaotic models can generally provide reliable and accurate short-term storm
surge predictions. |
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