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http://dx.doi.org/10.7731/KIFSE.2019.33.6.072

A Study on Accident Prediction Models for Chemical Accidents Using the Logistic Regression Analysis Model  

Lee, Tae-Hyung (National Institute of Chemical Safety)
Park, Choon-Hwa (National Institute of Chemical Safety)
Park, Hyo-Hyeon (National Institute of Chemical Safety)
Kwak, Dae-Hoon (School of Integrated National Security & Dept. of Crime & Forensic Science, Chungnam National Univ.)
Publication Information
Fire Science and Engineering / v.33, no.6, 2019 , pp. 72-79 More about this Journal
Abstract
Through this study, we developed a model for predicting chemical accidents lead to casualties. The model was derived from the logistic regression analysis model and applied to the variables affecting the accident. The accident data used in the model was analyzed by studying the statistics of past chemical accidents, and applying independent variables that were statistically significant through data analysis, such as the type of accident, cause, place of occurrence, status of casualties, and type of chemical accident that caused the casualties. A significance of p < 0.05 was applied. The model developed in this study is meaningful for the prevention of casualties caused by chemical accidents and the establishment of safety systems in the workplace. The analysis using the model found that the most influential factor in the occurrence of casualty in accidents was chemical explosions. Therefore, there is an urgent need to prepare countermeasures to prevent chemical accidents, specifically explosions, from occurring in the workplace.
Keywords
Chemical accident; Accident prediction; Logistic regression; Casualty accident;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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