Risk assessment of atmospheric hazard releases using K-means clustering

Guido Cervone; Pasquale Franzese; Yasmin Ezber; Zafer Boybeyi

doi:10.1109/ICDMW.2008.89

Risk assessment of atmospheric hazard releases using K-means clustering

Guido Cervone^*, Pasquale Franzese, Yasmin Ezber, Zafer Boybeyi

^*Corresponding author for this work

Eurasia Institute of Earth Sciences

George Mason University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

Unsupervised machine learning algorithms are used to perform statistical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions. A clustering algorithm is used to automatically group the results of the transport and dispersion simulations according to their respective cloud characteristics. Each cluster of clouds describes a distinct area at risk from potentially hazardous atmospheric contamination. Overimposing the resulting risk areas with ground maps, it is possible to assess the impact of the population exposure to the contaminants. The releases were simulated in the Bosphorus channel. Simulations were performed for one year at weekly interval, both day and night, to sample all different potential atmospheric conditions.

Original language	English
Title of host publication	Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008
Pages	342-348
Number of pages	7
DOIs	https://doi.org/10.1109/ICDMW.2008.89
Publication status	Published - 2008
Event	IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008 - Pisa, Italy Duration: 15 Dec 2008 → 19 Dec 2008

Publication series

Name	Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008

Conference

Conference	IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008
Country/Territory	Italy
City	Pisa
Period	15/12/08 → 19/12/08

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10.1109/ICDMW.2008.89

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Cervone, G., Franzese, P., Ezber, Y., & Boybeyi, Z. (2008). Risk assessment of atmospheric hazard releases using K-means clustering. In Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008 (pp. 342-348). Article 4733954 (Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008). https://doi.org/10.1109/ICDMW.2008.89

@inproceedings{eec82f2a0f314e59836f3d2fdd6020bf,

title = "Risk assessment of atmospheric hazard releases using K-means clustering",

abstract = "Unsupervised machine learning algorithms are used to perform statistical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions. A clustering algorithm is used to automatically group the results of the transport and dispersion simulations according to their respective cloud characteristics. Each cluster of clouds describes a distinct area at risk from potentially hazardous atmospheric contamination. Overimposing the resulting risk areas with ground maps, it is possible to assess the impact of the population exposure to the contaminants. The releases were simulated in the Bosphorus channel. Simulations were performed for one year at weekly interval, both day and night, to sample all different potential atmospheric conditions.",

author = "Guido Cervone and Pasquale Franzese and Yasmin Ezber and Zafer Boybeyi",

year = "2008",

doi = "10.1109/ICDMW.2008.89",

language = "English",

isbn = "9780769535036",

series = "Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008",

pages = "342--348",

booktitle = "Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008",

note = "IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008 ; Conference date: 15-12-2008 Through 19-12-2008",

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Cervone, G, Franzese, P, Ezber, Y & Boybeyi, Z 2008, Risk assessment of atmospheric hazard releases using K-means clustering. in Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008., 4733954, Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008, pp. 342-348, IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008, Pisa, Italy, 15/12/08. https://doi.org/10.1109/ICDMW.2008.89

Risk assessment of atmospheric hazard releases using K-means clustering. / Cervone, Guido; Franzese, Pasquale; Ezber, Yasmin et al.
Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008. 2008. p. 342-348 4733954 (Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Unsupervised machine learning algorithms are used to perform statistical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions. A clustering algorithm is used to automatically group the results of the transport and dispersion simulations according to their respective cloud characteristics. Each cluster of clouds describes a distinct area at risk from potentially hazardous atmospheric contamination. Overimposing the resulting risk areas with ground maps, it is possible to assess the impact of the population exposure to the contaminants. The releases were simulated in the Bosphorus channel. Simulations were performed for one year at weekly interval, both day and night, to sample all different potential atmospheric conditions.

AB - Unsupervised machine learning algorithms are used to perform statistical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions. A clustering algorithm is used to automatically group the results of the transport and dispersion simulations according to their respective cloud characteristics. Each cluster of clouds describes a distinct area at risk from potentially hazardous atmospheric contamination. Overimposing the resulting risk areas with ground maps, it is possible to assess the impact of the population exposure to the contaminants. The releases were simulated in the Bosphorus channel. Simulations were performed for one year at weekly interval, both day and night, to sample all different potential atmospheric conditions.

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BT - Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008

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ER -

Risk assessment of atmospheric hazard releases using K-means clustering

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