DOI : https://doi.org/10.18517/ijaseit.8.4-2.5759

Spatial Clustering based Meteorological Fields Construction for Regional Vulnerability Assessment

Taemin Lee (1), Woosung Choi (2), Jongryuel Sohn (3), Kyongwhan Moon (4), Sanghoon Byeon (5), Wookyun Lee (6), Soonyoung Jung (7)
(1) Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul 02841, Republic of Korea
(2) Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul 02841, Republic of Korea
(3) Department of Health and Environmental Science, College of Health Science, Korea University, Seoul 02841, Republic of Korea
(4) Department of Health and Environmental Science, College of Health Science, Korea University, Seoul 02841, Republic of Korea
(5) Department of Health and Environmental Science, College of Health Science, Korea University, Seoul 02841, Republic of Korea
(6) Department of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, Republic of Korea
(7) Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul 02841, Republic of Korea
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How to cite (IJASEIT) :
Lee, Taemin, et al. “Spatial Clustering Based Meteorological Fields Construction for Regional Vulnerability Assessment”. International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 4-2, Sept. 2018, pp. 1686-91, doi:10.18517/ijaseit.8.4-2.5759.
Citation Format :
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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