Probabilistic accident consequence uncertainty analysis: Dispersion and deposition uncertainty assessment, main report

Harper, F.T.; Young, M.G.; Miller, L.A.; Hora, Stephen C.; Lui, C.H.; Goossens, Louis; Cooke, RM; Paesler-Sauer, J.; Helton, Jon C.

doi:10.2172/10125585

Cited by 12 publications

(18 citation statements)

References 5 publications

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“…It aims at constructing a weighted combination of experts' probability assessments for each variable estimated. ( 2 , 4 –6 , 13 ) Experts can be weighted equally, or according to their (relative) expertise, typically indicated by their performance on seed variables.…”

Section: Methodsmentioning

confidence: 99%

“…Seed variables are variables the true values or realizations of which are unknown to the experts but known to the analyst at the time of the elicitation, or become known post hoc. ( 2 , 4–6 , 13 ) The experts should be able to adequately state their uncertainty on these variables. As the individual experts' performance on the seed variables is taken as an indication of their performance on the target variables, the seed variables must resemble the target variables as much as possible.…”

Section: Methodsmentioning

confidence: 99%

“…As the individual experts' performance on the seed variables is taken as an indication of their performance on the target variables, the seed variables must resemble the target variables as much as possible. ( 4–6 , 13 ) Ideally, the seed variables are randomly mixed with the target variables within the elicitation session, being indistinguishable from the target variables themselves. Domain variables are preferred, but if not available, adjacent variables could also be used.…”

Section: Methodsmentioning

confidence: 99%

“…( 1 , 2 ) Expert data obtained under rigorous methodological rules are increasingly being recognized as a valuable asset in numerous scientific fields ( 3–5 ) , including chemistry, nuclear sciences, seismic, and civil applications. ( 5–10 )…”

Section: Introductionmentioning

confidence: 99%

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Elicitation of Quantitative Data from a Heterogeneous Expert Panel: Formal Process and Application in Animal Health

et al. 2002

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This paper presents a protocol for a formal expert judgment process using a heterogeneous expert panel aimed at the quantification of continuous variables. The emphasis is on the process's requirements related to the nature of expertise within the panel, in particular the heterogeneity of both substantive and normative expertise. The process provides the opportunity for interaction among the experts so that they fully understand and agree upon the problem at hand, including qualitative aspects relevant to the variables of interest, prior to the actual quantification task. Individual experts' assessments on the variables of interest, cast in the form of subjective probability density functions, are elicited with a minimal demand for normative expertise. The individual experts' assessments are aggregated into a single probability density function per variable, thereby weighting the experts according to their expertise. Elicitation techniques proposed include the Delphi technique for the qualitative assessment task and the ELI method for the actual quantitative assessment task. Appropriately, the Classical model was used to weight the experts' assessments in order to construct a single distribution per variable. Applying this model, the experts' quality typically was based on their performance on seed variables. An application of the proposed protocol in the broad and multidisciplinary field of animal health is presented. Results of this expert judgment process showed that the proposed protocol in combination with the proposed elicitation and analysis techniques resulted in valid data on the (continuous) variables of interest. In conclusion, the proposed protocol for a formal expert judgment process aimed at the elicitation of quantitative data from a heterogeneous expert panel provided satisfactory results. Hence, this protocol might be useful for expert judgment studies in other broad and/or multidisciplinary fields of interest.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Elicitation of Quantitative Data from a Heterogeneous Expert Panel: Formal Process and Application in Animal Health

et al. 2002

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“…HORA and IMAN (1989) outlined a formal approach for eliciting information from experts. HARPER et al (1994) and Cook et al (1994) discussed this approach, application, and results for the dispersion and deposition uncertainty assessment in the probabilistic uncertainty analysis of radiological accident consequences. HANNA et al (1998) utilized an informal elicitation method based on responses from ten experts to specify the uncertainties in 109 input parameters, including those related to emissions, meteorology and boundary conditions for a photochemical grid model.…”

Section: Expert Judgmentmentioning

confidence: 99%

Uncertainty Analysis in Atmospheric Dispersion Modeling

Rao

2005

Pure appl. geophys.

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The concentration of a pollutant in the atmosphere is a random variable that cannot be predicted accurately, but can be described using quantities such as ensemble mean, variance, and probability distribution. There is growing recognition that the modeled concentrations of hazardous contaminants in the atmosphere should be described in a probabilistic framework. This paper discusses the various types of uncertainties in atmospheric dispersion models, and reviews sensitivity/uncertainty analysis methods to characterize and/or reduce them. Evaluation and quantification of the range of uncertainties in predictions yield a deeper insight into the capabilities and limitations of atmospheric dispersion models, and increase our confidence in decision-making based on models.

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Experience with Expert Judgment: theTUDelft Expert Judgment Data

Cooke

Goossens

2008

Wiley Handbook of Science and Technology for Homeland Security

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We review the applications of structured expert judgment uncertainty quantification using the “classical model” developed at the Delft University of Technology over the last 17 years. These involve 45 expert panels, performed under contract with problem owners who reviewed and approved the results. With a few exceptions, all these applications involved the use of seed variables; that is, variables from the experts' area of expertise for which the true values are available post hoc. Seed variables are used to (i) measure expert performance, (ii) enable performance‐based weighted combination of experts' distributions, and (iii) evaluate and hopefully validate the resulting combination or “decision maker”. This article reviews the classical model for structured expert judgment and the performance measures, reviews applications, comparing performance‐based decision makers with “equal weight” decision makers, and discusses some lessons learned.

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Probabilistic accident consequence uncertainty analysis: Dispersion and deposition uncertainty assessment, main report

Abstract: Neither the Commission of the European Communities nor any person acting on behalf of the Commission is responsible for the use which might be made of the following information.

Cited by 12 publications

References 5 publications

Elicitation of Quantitative Data from a Heterogeneous Expert Panel: Formal Process and Application in Animal Health

Elicitation of Quantitative Data from a Heterogeneous Expert Panel: Formal Process and Application in Animal Health

Uncertainty Analysis in Atmospheric Dispersion Modeling

Experience with Expert Judgment: theTUDelft Expert Judgment Data

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