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

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 o...

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Autores principales: Yuan, Changfeng, Zhang, Yulong, Wang, Jiahui, Tong, Yating
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463584/
https://www.ncbi.nlm.nih.gov/pubmed/34561467
http://dx.doi.org/10.1038/s41598-021-97785-4
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author Yuan, Changfeng
Zhang, Yulong
Wang, Jiahui
Tong, Yating
author_facet Yuan, Changfeng
Zhang, Yulong
Wang, Jiahui
Tong, Yating
author_sort Yuan, Changfeng
collection PubMed
description 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.
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spelling pubmed-84635842021-09-27 Modeling and evaluation of causal factors in emergency responses to fire accidents involving oil storage system Yuan, Changfeng Zhang, Yulong Wang, Jiahui Tong, Yating Sci Rep Article 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. Nature Publishing Group UK 2021-09-24 /pmc/articles/PMC8463584/ /pubmed/34561467 http://dx.doi.org/10.1038/s41598-021-97785-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Yuan, Changfeng
Zhang, Yulong
Wang, Jiahui
Tong, Yating
Modeling and evaluation of causal factors in emergency responses to fire accidents involving oil storage system
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463584/
https://www.ncbi.nlm.nih.gov/pubmed/34561467
http://dx.doi.org/10.1038/s41598-021-97785-4
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