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| Titel |
An attempt to model the relationship between MMI attenuation and engineering ground-motion parameters using artificial neural networks and genetic algorithms |
| VerfasserIn |
G.-A. Tselentis, L. Vladutu |
| 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 ; 10, no. 12 ; Nr. 10, no. 12 (2010-12-07), S.2527-2537 |
| Datensatznummer |
250008532
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| Publikation (Nr.) |
 copernicus.org/nhess-10-2527-2010.pdf |
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| Zusammenfassung |
| Complex application domains involve difficult pattern classification
problems. This paper introduces a model of MMI attenuation and its
dependence on engineering ground motion parameters based on artificial
neural networks (ANNs) and genetic algorithms (GAs). The ultimate goal of
this investigation is to evaluate the target-region applicability of
ground-motion attenuation relations developed for a host region based on
training an ANN using the seismic patterns of the host region. This ANN
learning is based on supervised learning using existing data from past
earthquakes. The combination of these two learning procedures (that is, GA
and ANN) allows us to introduce a new method for pattern recognition in the
context of seismological applications. The performance of this new GA-ANN
regression method has been evaluated using a Greek seismological database
with satisfactory results. |
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