A systematical Bayesian approach for outcome-oriented fire service data collection. (November 2015)

• Record Type: Journal Article
• Authors: De Sanctis, G.; Ridder, A.; Barth, U.; Fontana, M.
• Is Part Of: Fire safety journal. Volume 78(2015:Nov.)
• Page Start: 219
• Page End: 228
• Publication Date: 2015-11
• Subjects: Fire service data -- Value of information -- Probabilistic approach -- Bayesian inference; Fire prevention -- Periodicals; Incendies -- Prévention -- Recherche -- Périodiques; Fire prevention -- Research; Periodicals; 628.92205
• DOI: 10.1016/j.firesaf.2015.09.002
• Languages: English
• ISSNs: 0379-7112

Abstract

Abstract: Data collection is always related to costs and to investments of resources. Especially data collected by the fire services is often not collected in a systematic manner and often without a clear purpose. A more systematic approach is achieved by identifying the stakeholders or decision-makers who are intended to use or benefit from the data and bear the costs. Whether a survey is reasonable or not can be treated in the context of a decision-problem by performing a pre-posterior decision analysis in order to assess the value of information. Potential stakeholders are identified that can benefit from fire service data and an overview is provided on the type of information that can be obtained by a survey or information that results from engineering knowledge. Both information are associated with uncertainties and should be identified and quantified before the data is collected to limit the extent of the collection process. A hierarchical Bayesian probabilistic approach is proposed to combine both type of information by differentiate the uncertainties between aleatory and epistemic uncertainties. The data can be used to update epistemic uncertainties by applying Bayesian inference techniques. An example for the estimation of fire service intervention characteristics illustrates the approach and discusses how aleatory and epistemic uncertainties can be quantified. Highlights: Identification of stakeholders benefiting from data collection. Pre-posterior decision-analysis to evaluate the value of information. Hierarchical probabilistic model to address aleatory and epistemic uncertainties. Updating epistemic uncertainties by Bayesian inference techniques. Linking databases reduce the data collection effort and provide additional data.