Risk assessment of atmospheric emissions using machine learning

Risk assessment of atmospheric emissions using machine learning

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. <br><br> First, a clustering al...

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Bibliographic Details
Main Authors: G. Cervone, P. Franzese, Y. Ezber, Z. Boybeyi
Format: Article
Language:English
Published: Copernicus Publications 2008-09-01
Series:Natural Hazards and Earth System Sciences
Online Access:http://www.nat-hazards-earth-syst-sci.net/8/991/2008/nhess-8-991-2008.pdf
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spelling doaj-061ed9b7ad3c4c92bce955fe7d5ac7bd2020-11-24T23:32:26ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812008-09-01859911000Risk assessment of atmospheric emissions using machine learningG. CervoneP. FranzeseY. EzberZ. BoybeyiSupervised 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. <br><br> 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. <br><br> 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. http://www.nat-hazards-earth-syst-sci.net/8/991/2008/nhess-8-991-2008.pdf
collection DOAJ
language English
format Article
sources DOAJ
author G. Cervone
P. Franzese
Y. Ezber
Z. Boybeyi
spellingShingle G. Cervone
P. Franzese
Y. Ezber
Z. Boybeyi
Risk assessment of atmospheric emissions using machine learning
Natural Hazards and Earth System Sciences
author_facet G. Cervone
P. Franzese
Y. Ezber
Z. Boybeyi
author_sort G. Cervone
title Risk assessment of atmospheric emissions using machine learning
title_short Risk assessment of atmospheric emissions using machine learning
title_full Risk assessment of atmospheric emissions using machine learning
title_fullStr Risk assessment of atmospheric emissions using machine learning
title_full_unstemmed Risk assessment of atmospheric emissions using machine learning
title_sort risk assessment of atmospheric emissions using machine learning
publisher Copernicus Publications
series Natural Hazards and Earth System Sciences
issn 1561-8633
1684-9981
publishDate 2008-09-01
description 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. <br><br> 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. <br><br> 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.
url http://www.nat-hazards-earth-syst-sci.net/8/991/2008/nhess-8-991-2008.pdf
work_keys_str_mv AT gcervone riskassessmentofatmosphericemissionsusingmachinelearning
AT pfranzese riskassessmentofatmosphericemissionsusingmachinelearning
AT yezber riskassessmentofatmosphericemissionsusingmachinelearning
AT zboybeyi riskassessmentofatmosphericemissionsusingmachinelearning
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