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| Titel |
Novel method for hurricane trajectory prediction based on data mining |
| VerfasserIn |
X. Dong, D. C. Pi |
| 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 ; 13, no. 12 ; Nr. 13, no. 12 (2013-12-10), S.3211-3220 |
| Datensatznummer |
250085580
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| Publikation (Nr.) |
 copernicus.org/nhess-13-3211-2013.pdf |
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| Zusammenfassung |
| This paper describes a novel method for hurricane trajectory prediction based
on data mining (HTPDM) according to the hurricane's motion characteristics.
Firstly, all frequent trajectories in the historical hurricane trajectory
database are mined by using association analysis technology and their
corresponding association rules are generated as motion patterns. Then, the
current hurricane trajectories are matched with the motion patterns for
predicting. If no association rule is found for matching, a predicted result
according to the hurricane current movement trend would be returned. All
experiments are conducted with the Atlantic weather Hurricane/Tropical Data
from 1900 to 2008. The experimental results show that if the matching failure
part is contained, the prediction accuracy is 57.5%. Whereas, the valve
would be to 65% provided all matches are successful. |
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