Spatial clustering based meteorological fields construction for regional vulnerability assessment

Research output: Contribution to journalArticle

Abstract

Chemical accidents have affected the social-environmental system. For the regional vulnerability assessment, which is the baseline work to assess the impact on the environment, a meteorological field is needed to determine how chemicals from multiple adjacent companies are propagated. In this study, we present the method of meteorological field based on the spatial cluster which is the main component of vulnerability assessment on regional chemical accident scenario. To integrate spatially dense chemical companies into a cluster, we adopt spatial clustering algorithms. Experiment result shows that DBSCAN-based approach reduces 80.5% total area of the meteorological field against brute-force algorithm, and shows good performance on the average of the overlap ratio, and utility ratio for clustering results.

Original languageEnglish
Pages (from-to)1686-1691
Number of pages6
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume8
Issue number4-2
Publication statusPublished - 2018 Jan 1

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Chemical Hazard Release
accidents
Cluster Analysis
Accidents
Clustering algorithms
Industry
Experiments
methodology

Keywords

  • DBSCAN
  • Meteorological field
  • Spatial clustering
  • Vulnerability assessment

ASJC Scopus subject areas

  • Computer Science(all)
  • Agricultural and Biological Sciences(all)
  • Engineering(all)

Cite this

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title = "Spatial clustering based meteorological fields construction for regional vulnerability assessment",
abstract = "Chemical accidents have affected the social-environmental system. For the regional vulnerability assessment, which is the baseline work to assess the impact on the environment, a meteorological field is needed to determine how chemicals from multiple adjacent companies are propagated. In this study, we present the method of meteorological field based on the spatial cluster which is the main component of vulnerability assessment on regional chemical accident scenario. To integrate spatially dense chemical companies into a cluster, we adopt spatial clustering algorithms. Experiment result shows that DBSCAN-based approach reduces 80.5{\%} total area of the meteorological field against brute-force algorithm, and shows good performance on the average of the overlap ratio, and utility ratio for clustering results.",
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AU - Lee, Woo-Kyun

AU - Jung, Soon Young

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