A Study on the Selection of Inspection Targets of Chemical Accident Prevention Management Plan Based on Big Data 


Vol. 48,  No. 10, pp. 1247-1253, Oct.  2023
10.7840/kics.2023.48.10.1247


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  Abstract

The expansion in the use of chemicals due to industrial advancement increases the possibility of chemical accidents caused by chemicals. Therefore, measures for effective prevention and response to chemical accidents have been studied in order to minimize social damage to public health, property, and environment. In particular, regulations on chemical accident management and prevention plans integrating risk management schemes for accident preparation material handling facilities and impact management schemes for human life and the environment were enacted to prevent chemical accidents. In this study, we proposed a study on how to select implementation inspection organizations by using big data-based machine learning method for hazardous chemical handling organizations that submitted chemical accident prevention management plans.

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[IEEE Style]

W. Jeong, Y. J. Kim, T. I. Ryu, S. Jo, G. Kim, "A Study on the Selection of Inspection Targets of Chemical Accident Prevention Management Plan Based on Big Data," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 10, pp. 1247-1253, 2023. DOI: 10.7840/kics.2023.48.10.1247.

[ACM Style]

Weonil Jeong, Yeon Jin Kim, Tae In Ryu, Seungbum Jo, and Gyoung-Bae Kim. 2023. A Study on the Selection of Inspection Targets of Chemical Accident Prevention Management Plan Based on Big Data. The Journal of Korean Institute of Communications and Information Sciences, 48, 10, (2023), 1247-1253. DOI: 10.7840/kics.2023.48.10.1247.

[KICS Style]

Weonil Jeong, Yeon Jin Kim, Tae In Ryu, Seungbum Jo, Gyoung-Bae Kim, "A Study on the Selection of Inspection Targets of Chemical Accident Prevention Management Plan Based on Big Data," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 10, pp. 1247-1253, 10. 2023. (https://doi.org/10.7840/kics.2023.48.10.1247)
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