Risk assessment of atmospheric emissions using machine learning

G. Cervone, P. Franzese, Y. Ezber, Z. Boybeyi

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)991-1000
Number of pages10
JournalNatural Hazards and Earth System Science
Volume8
Issue number5
DOIs
StatePublished - Sep 1 2008

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)

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