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

Geography

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

3 Scopus citations

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 (US)
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
State	Published - 2008
Event	IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008 - Pisa, Italy Duration: Dec 15 2008 → Dec 19 2008

Publication series

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

Other

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

All Science Journal Classification (ASJC) codes

Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/ICDMW.2008.89

Cite this

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{507b24a7bf5d480bb7e3a1873d21a8f9,

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 (US)",

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",

}

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, 12/15/08. 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

TY - GEN

T1 - Risk assessment of atmospheric hazard releases using K-means clustering

AU - Cervone, Guido

AU - Franzese, Pasquale

AU - Ezber, Yasmin

AU - Boybeyi, Zafer

PY - 2008

Y1 - 2008

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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M3 - Conference contribution

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T3 - Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008

SP - 342

EP - 348

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

T2 - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008

Y2 - 15 December 2008 through 19 December 2008

ER -

Risk assessment of atmospheric hazard releases using K-means clustering

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