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Fire risk assessment in cotton storage based on fuzzy comprehensive evaluation and Bayesian network
Author(s) -
Chen Jinyue,
Ji Jie,
Ding Long,
Wu Jiansong
Publication year - 2020
Publication title -
fire and materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.482
H-Index - 58
eISSN - 1099-1018
pISSN - 0308-0501
DOI - 10.1002/fam.2832
Subject(s) - firefighting , bayesian network , fire control , engineering , risk assessment , fire prevention , control (management) , risk control , risk analysis (engineering) , forensic engineering , risk management , operations research , computer science , transport engineering , computer security , architectural engineering , business , geography , cartography , finance , artificial intelligence
Summary Cotton storage fire develops rapidly, and it has high probability of being out of control and causing huge economic loss. In order to carry out a quantitative risk assessment of cotton storage fire accidents, a dynamic risk assessment model of cotton storage fire is established by means of data analysis, fuzzy comprehensive evaluation (FCE), cases study and expert opinions based on Bayesian network (BN). The model includes three sections (fire causes section, fire discoverer section, fire control section) and one mainline, with 22 nodes, and gives accident risk in the form of economic loss. Risk can be renewed quickly by giving new evidence to model nodes of BN. The application of FCE provides a feasible solution to solve the data deficiency problem in databases, experiment results and experience in model construction. Sensitivity analysis result shows firefighters participation, timely control of fire, firefighting systems and workers are important to put out the fire. A case study shows that the loss of a fire accident happened in an open storage yard is more serious than the loss of a similar fire accident happened in warehouse. This model provides support for cotton storage fire risk management and decision‐making in an emergency fire accident.