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| Titel |
Risk assessment of atmospheric emissions using machine learning |
| VerfasserIn |
G. Cervone, P. Franzese, Y. Ezber, Z. Boybeyi |
| 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 ; 8, no. 5 ; Nr. 8, no. 5 (2008-09-09), S.991-1000 |
| Datensatznummer |
250005763
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| Publikation (Nr.) |
 copernicus.org/nhess-8-991-2008.pdf |
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| Zusammenfassung |
Supervised and unsupervised machine learning algorithms are used to perform
statistical and logical analysis of several transport and dispersion model
runs which simulate emissions from a fixed source under different atmospheric
conditions.
First, a clustering algorithm is used to automatically group the results of
different transport and dispersion simulations according to specific cloud
characteristics. Then, a symbolic classification algorithm is employed to
find complex non-linear relationships between the meteorological input
conditions and each cluster of clouds. The patterns discovered are provided
in the form of probabilistic measures of contamination, thus suitable for
result interpretation and dissemination.
The learned patterns can be used for quick assessment of the areas at risk
and of the fate of potentially hazardous contaminants released in the
atmosphere. |
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