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Related Experiment Videos

Uncertainty in compartmental models for hazardous materials - a case study.

B C Kraan1, R M Cooke

  • 1TU Delft, Faculty of Mathematics, Mekelweg 4, P.O. Box 5031, 2600 GA, Delft, Netherlands.

Journal of Hazardous Materials
|February 29, 2000
PubMed
Summary

This study introduces uncertainty analysis for compartmental models using expert judgment. A probabilistic inversion technique transfers uncertainty from observable quantities to model parameters for better predictions.

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Area of Science:

  • Environmental Science
  • Risk Assessment
  • Computational Modeling

Background:

  • Compartmental models are crucial for simulating complex systems, but their predictions often carry significant uncertainty.
  • Quantifying uncertainty in these models is essential for reliable risk assessment, particularly in areas like accident consequence analysis.
  • Existing methods for uncertainty analysis face challenges in directly assessing unobservable model parameters.

Purpose of the Study:

  • To introduce a methodology for performing uncertainty analysis on compartmental models.
  • To present a probabilistic inversion technique for transferring uncertainty from observable quantities to model input parameters.
  • To illustrate the application of this technique using a specific human body compartmental model.

Main Methods:

Related Experiment Videos

  • Utilizing structured expert judgment to quantify uncertainty in physically observable quantities.
  • Developing a probabilistic inversion technique to link expert-assessed uncertainties to unobservable code input parameters.
  • Applying the technique to a compartmental model of strontium (Sr) retention in the human body.

Main Results:

  • Demonstrated a method to quantify uncertainty in compartmental models through expert elicitation.
  • Successfully transferred uncertainty from observable quantities to model parameters using probabilistic inversion.
  • Provided a practical example of uncertainty analysis in a human health context.

Conclusions:

  • Structured expert judgment combined with probabilistic inversion offers a robust approach to uncertainty analysis in compartmental models.
  • The developed technique effectively addresses the challenge of unobservable model parameters in accident consequence codes.
  • This methodology enhances the reliability of predictions from compartmental models in various scientific and safety applications.