A Three-Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property
| Autor: | R. Pritchard, Jin Wang, Ian Jenkinson, DB Matellini, Alan Wall |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Property (programming) business.industry media_common.quotation_subject Environmental resource management Posterior probability Bayesian network Extinguishment 020101 civil engineering 02 engineering and technology Ambiguity Fire risk 0201 civil engineering Accident investigation Physiology (medical) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Safety Risk Reliability and Quality business Soft data media_common |
| Zdroj: | Risk Analysis. 38:2087-2104 |
| ISSN: | 0272-4332 |
| Popis: | In the United Kingdom, dwelling fires are responsible for the majority of all fire-related fatalities. The development of these incidents involves the interaction of a multitude of variables that combine in many different ways. Consequently, assessment of dwelling fire risk can be complex, which often results in ambiguity during fire safety planning and decision making. In this article, a three-part Bayesian network model is proposed to study dwelling fires from ignition through to extinguishment in order to improve confidence in dwelling fire safety assessment. The model incorporates both hard and soft data, delivering posterior probabilities for selected outcomes. Case studies demonstrate how the model functions and provide evidence of its use for planning and accident investigation. |
| Databáze: | OpenAIRE |
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