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Fire Sci. Eng. > Volume 33(6); 2019 > Article
Fire Science and Engineering 2019;33(6):72-79.
DOI: https://doi.org/10.7731/KIFSE.2019.33.6.072    Published online December 31, 2019.
로지스틱회귀분석 모델을 활용한 화학사고 사상사고 예측모형 개발 연구
이태형1, 박춘화2, 박효현3, 곽대훈4
1화학물질안전원 공업연구사/충남대학교 과학수사학과 대학원생
2화학물질안전원 공업연구관
3화학물질안전원 주무관
4충남대학교 국가안보융합학부&과학수사학과 부교수
A Study on Accident Prediction Models for Chemical Accidents Using the Logistic Regression Analysis Model
Tae-Hyung Lee1, Choon-Hwa Park2, Hyo-Hyeon Park3, Dae-Hoon Kwak4
1Researcher, National Institute of Chemical Safety / Graduate Student, Dept. of Crime & Forensic Science, Chungnam National Univ.
2Senior Researcher, National Institute of Chemical Safety
3Researcher, National Institute of Chemical Safety
4Associate Professor, School of Integrated National Security & Dept. of Crime & Forensic Science, Chungnam National Univ.
Correspondence:  Dae-Hoon Kwak, Tel: +82-42-821-5298, Fax: +82-42-822-5236, 
Email: dkwak@cnu.ac.kr
Received: 26 August 2019   • Revised: 20 October 2019   • Accepted: 1 November 2019
요약
본 연구를 통해 화학사고 사상사고 예측모형을 개발하였다. 모형은 로지스틱회귀분석 모델을 활용하여 사상사고에 영향을 주는 변수를 도출하여 적용하였고, 통계적 검증방법과 오즈비를 활용하여 모형의 신뢰성 및 정확성을 검증하였다. 모형에 활용한 사고 자료는 과거 발생했던 화학사고 통계를 분석하여 활용하였으며, 사고의 유형, 원인, 발생 장소, 사상자 현황 및 사상자를 발생시킨 화학사고 등의 자료 분석을 통해 통계적으로 유의하게 나타난 독립변수(p < 0.05)를 적용하였다. 본 연구에서 개발한 모형은 사업장에서 화학사고로 인해 발생하는 사상사고의 예방 및 안전시스템 구축을 위한 연구로서 의의가 있다고 할 수 있다. 모형에 의한 분석결과 사상사고 발생에 가장 크게 영향을 미치는 변수는 폭발에 의한 화학사고인 것으로 조사되었다. 따라서 사업장에서 발생하는 폭발 유형의 화학사고를 예방하기 위한 대책마련이 시급하다고 판단된다.
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.
Key Words: Chemical accident, Accident prediction, Logistic regression, Casualty accident


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