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

^*Bu çalışma için yazışmadan sorumlu yazar

Avrasya Yer Bilimleri Enstitüsü

College of Science
George Mason University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

3 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008
Sayfalar	342-348
Sayfa sayısı	7
DOI'lar	https://doi.org/10.1109/ICDMW.2008.89
Yayın durumu	Yayınlandı - 2008
Etkinlik	IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008 - Pisa, Italy Süre: 15 Ara 2008 → 19 Ara 2008

Yayın serisi

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

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008
Ülke/Bölge	Italy
Şehir	Pisa
Periyot	15/12/08 → 19/12/08

Belgeye Erişim

10.1109/ICDMW.2008.89

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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). Makale 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",

}

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).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

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.

UR - https://www.scopus.com/pages/publications/62449300587

U2 - 10.1109/ICDMW.2008.89

DO - 10.1109/ICDMW.2008.89

M3 - Conference contribution

AN - SCOPUS:62449300587

SN - 9780769535036

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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