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| Titel |
Relationship between isoseismal area and magnitude of historical earthquakes in Greece by a hybrid fuzzy neural network method |
| VerfasserIn |
G.-A. Tselentis, E. Sokos |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 12, no. 1 ; Nr. 12, no. 1 (2012-01-04), S.37-45 |
| Datensatznummer |
250010401
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| Publikation (Nr.) |
 copernicus.org/nhess-12-37-2012.pdf |
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| Zusammenfassung |
| In this paper we suggest the use of diffusion-neural-networks, (neural
networks with intrinsic fuzzy logic abilities) to assess the relationship
between isoseismal area and earthquake magnitude for the region of Greece.
It is of particular importance to study historical earthquakes for which
we often have macroseismic information in the form of isoseisms but
it is statistically incomplete to assess magnitudes from an isoseismal area or to
train conventional artificial neural networks for magnitude
estimation. Fuzzy relationships are developed and used to train a feed forward neural
network with a back propagation algorithm to obtain the final relationships.
Seismic intensity data from 24 earthquakes in Greece have been used. Special
attention is being paid to the incompleteness and contradictory patterns in
scanty historical earthquake records. The results show that the proposed
processing model is very effective, better than applying classical
artificial neural networks since the magnitude macroseismic intensity
target function has a strong nonlinearity and in most cases the macroseismic
datasets are very small. |
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