Risk assessment of atmospheric hazard releases using K-means clustering

Guido Cervone, Pasquale Franzese, Yasmin Ezber, Zafer Boybeyi

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008
Pages342-348
Number of pages7
DOIs
StatePublished - Dec 1 2008
EventIEEE International Conference on Data Mining Workshops, ICDM Workshops 2008 - Pisa, Italy
Duration: Dec 15 2008Dec 19 2008

Other

OtherIEEE International Conference on Data Mining Workshops, ICDM Workshops 2008
CountryItaly
CityPisa
Period12/15/0812/19/08

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • 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). [4733954] https://doi.org/10.1109/ICDMW.2008.89