Modeling and evaluation of causal factors in emergency responses to fire accidents involving oil storage system

Abstract According to the statistics of 160 typical fire and explosion accidents in oil storage areas at home and abroad nearly 50 years, 122 of them occurred the secondary accidents in the emergency responses. Based on 122 accident cases, 21 causal factors leading to secondary accidents are summari...

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Autores principales:	Changfeng Yuan, Yulong Zhang, Jiahui Wang, Yating Tong
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8b8e1e1eef24481c8da5b70631fb476e
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spelling	oai:doaj.org-article:8b8e1e1eef24481c8da5b70631fb476e2021-12-02T18:48:02ZModeling and evaluation of causal factors in emergency responses to fire accidents involving oil storage system10.1038/s41598-021-97785-42045-2322https://doaj.org/article/8b8e1e1eef24481c8da5b70631fb476e2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97785-4https://doaj.org/toc/2045-2322Abstract According to the statistics of 160 typical fire and explosion accidents in oil storage areas at home and abroad nearly 50 years, 122 of them occurred the secondary accidents in the emergency responses. Based on 122 accident cases, 21 causal factors leading to secondary accidents are summarized. In order to quantify the influencing degree of these causal factors on the accident consequences, a multiple linear regression model was established between them. In the modeling process, these factors are decomposed into the criterion layer, variable layer, and bottom layer. The improved analytic hierarchy process (IAHP) was used to establish the relationship between the bottom factors and variable factors, and the regression analysis method was used to establish the relational model between variable layer and criterion layer. For 122 cases of the secondary accidents, this study took the year as a statistical dimension, and obtained 40 groups of sample data. The first 34 groups of sample data were used to build the causal factors model, and the last 6 groups of sample data were tested the generalization ability of the model by using the established regression model combined with grey prediction model. The results show that the prediction ability of the established model was better than that of the grey prediction model alone. Moreover, the relative contribution and change trend of the causal factors were evaluated using the mutation progression method, and corresponding preventive countermeasures were proposed. It was found that human professional skills, knowledge and literacy, environmental issues, and firefighting facilities are the main influencing factors that lead to the secondary accidents. These three kinds of factors show a gradual improvement trend, and the existing prevention measures should be maintained and further improved. The problem of inherent objects or equipment factors has not been effectively improved and has a worsening trend, which is the focus of prevention in the future, and the prevention and control efforts need to be moderately increased. The research results have important guiding significance for understanding the quantitative influences of causal factors on the accident consequences, improving emergency response capabilities, reducing accident losses, and avoiding secondary accidents.Changfeng YuanYulong ZhangJiahui WangYating TongNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Changfeng Yuan Yulong Zhang Jiahui Wang Yating Tong Modeling and evaluation of causal factors in emergency responses to fire accidents involving oil storage system
description	Abstract According to the statistics of 160 typical fire and explosion accidents in oil storage areas at home and abroad nearly 50 years, 122 of them occurred the secondary accidents in the emergency responses. Based on 122 accident cases, 21 causal factors leading to secondary accidents are summarized. In order to quantify the influencing degree of these causal factors on the accident consequences, a multiple linear regression model was established between them. In the modeling process, these factors are decomposed into the criterion layer, variable layer, and bottom layer. The improved analytic hierarchy process (IAHP) was used to establish the relationship between the bottom factors and variable factors, and the regression analysis method was used to establish the relational model between variable layer and criterion layer. For 122 cases of the secondary accidents, this study took the year as a statistical dimension, and obtained 40 groups of sample data. The first 34 groups of sample data were used to build the causal factors model, and the last 6 groups of sample data were tested the generalization ability of the model by using the established regression model combined with grey prediction model. The results show that the prediction ability of the established model was better than that of the grey prediction model alone. Moreover, the relative contribution and change trend of the causal factors were evaluated using the mutation progression method, and corresponding preventive countermeasures were proposed. It was found that human professional skills, knowledge and literacy, environmental issues, and firefighting facilities are the main influencing factors that lead to the secondary accidents. These three kinds of factors show a gradual improvement trend, and the existing prevention measures should be maintained and further improved. The problem of inherent objects or equipment factors has not been effectively improved and has a worsening trend, which is the focus of prevention in the future, and the prevention and control efforts need to be moderately increased. The research results have important guiding significance for understanding the quantitative influences of causal factors on the accident consequences, improving emergency response capabilities, reducing accident losses, and avoiding secondary accidents.
format	article
author	Changfeng Yuan Yulong Zhang Jiahui Wang Yating Tong
author_facet	Changfeng Yuan Yulong Zhang Jiahui Wang Yating Tong
author_sort	Changfeng Yuan
title	Modeling and evaluation of causal factors in emergency responses to fire accidents involving oil storage system
title_short	Modeling and evaluation of causal factors in emergency responses to fire accidents involving oil storage system
title_full	Modeling and evaluation of causal factors in emergency responses to fire accidents involving oil storage system
title_fullStr	Modeling and evaluation of causal factors in emergency responses to fire accidents involving oil storage system
title_full_unstemmed	Modeling and evaluation of causal factors in emergency responses to fire accidents involving oil storage system
title_sort	modeling and evaluation of causal factors in emergency responses to fire accidents involving oil storage system
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8b8e1e1eef24481c8da5b70631fb476e
work_keys_str_mv	AT changfengyuan modelingandevaluationofcausalfactorsinemergencyresponsestofireaccidentsinvolvingoilstoragesystem AT yulongzhang modelingandevaluationofcausalfactorsinemergencyresponsestofireaccidentsinvolvingoilstoragesystem AT jiahuiwang modelingandevaluationofcausalfactorsinemergencyresponsestofireaccidentsinvolvingoilstoragesystem AT yatingtong modelingandevaluationofcausalfactorsinemergencyresponsestofireaccidentsinvolvingoilstoragesystem
_version_	1718377636925800448

Modeling and evaluation of causal factors in emergency responses to fire accidents involving oil storage system

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