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Approaches to visualisation of uncertainties to decision makers in an operational Decision Support System   W. Raskob 1 , F. Gering 2 , V. Bertsch 3 1   Forschungszentrum Karlsruhe, IKET, Karlsruhe, Germany 2  Federal Office for Radiation Protection, Neuherberg, Germany   3  Karlsruhe Institute of Technology   , Karlsruhe, Germany ISCRAM 2009, 10.-13-05.2009
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
 
Exposure during and after a nuclear accident Dose rate Time Accident happened Hours Days Weeks Months Years R. Mustonen Ingested with water Inhaled from plume External from plume External from deposition Ingested with food Total
Information processing in RODOS Meteorological and Release Data, Radiological Monitoring Data GIS Data, National Data Base, Scenario Data 0 Environmental Contamination of Air, Ground, and Food,  Potential Doses Radiological Situation: real-time Diagnosis + Prognosis 1 Areas, Organ Doses, People affected by Countermeasures, Health Effects, Effort and Cost Countermeasures: Strategies and Consequences   2 Ranked List of feasible Strategies of long-term countermeasures (Decision Analysis) Evaluation   of Strategies 3
Different types of uncertainty are of different importance in the different phases of emergency management Data Uncertainties Parameter (preferential) Uncertainties t ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Emergency Intermediate Phase:  Measurement Uncertainties
Issues in the early (pre-release) phase ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Decisions have to be taken with very uncertain input to init i ate evacuation, sheltering or distribution of stable iodine
Typical result of dose model ,[object Object]
Comparison of on-site and prognostic weather data ,[object Object],[object Object],[object Object]
Model parameters Model input Uncertainty of model parameters Uncertainty of model input Ensembles Uncertainty modelling ,[object Object],[object Object],probability density model parameters
Ensemble calculations ,[object Object],[object Object],[object Object]
Communication of results in RODOS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Visualisation of uncertainties (2D) ,[object Object],[object Object]
Proposed visualisation ,[object Object],[object Object],[object Object]
Issues in the  later phase ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Decisions have to be taken with relative certain input but other ‚soft‘ factors have to be taken into account
[object Object],[object Object],[object Object],[object Object],Decision making in the context of  emergency management
Problem Structuring aims at  hierarchically modelling the decision  criteria overall goal overall objective logistics dose sub-objectives collective dose  saved individual dose  saved waste work effort attributes strategy  y strategy  x strategy  z alternatives
Web-HIPRE provides various preference elicitation methods The direct weighting dialog Preference Elicitation The “SMART” weighting method The “SWING” weighting dialog
The communication of the results is accompanied by sensitivity analyses in Web-HIPRE The  composite priorities  illustrate the results of the analysis and the contributions of the different criteria to the overall results Aggregation Sensitivity  Analysis Sensitivity analyses  show the effect of changing the weight of an objective and give an overall assessment of the decision parameters
Example data for Web-Hipre ,[object Object],[object Object],[object Object],log-normal normal Distribution  x 1.2 - 4° 10 x 488 + 48° 9 x 0.9 - 24° 8 x 5.1 + 6° 7 x 0.007 - 40° 6 x 0.02 - 29° 5 x 100    0° 4 x 1.0 + 30° 3 x 0.01    0° 2 x 1.0    0° 1 Deviation from  mean source  term Deviation from  mean wind  direction Example ensembles
Composite priorities ,[object Object],[object Object]
Sensitivity analysis ,[object Object]
Conclusions and future steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object]

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Raskob Iscram 2009

  • 1. Approaches to visualisation of uncertainties to decision makers in an operational Decision Support System W. Raskob 1 , F. Gering 2 , V. Bertsch 3 1 Forschungszentrum Karlsruhe, IKET, Karlsruhe, Germany 2 Federal Office for Radiation Protection, Neuherberg, Germany 3 Karlsruhe Institute of Technology , Karlsruhe, Germany ISCRAM 2009, 10.-13-05.2009
  • 2.
  • 3.  
  • 4. Exposure during and after a nuclear accident Dose rate Time Accident happened Hours Days Weeks Months Years R. Mustonen Ingested with water Inhaled from plume External from plume External from deposition Ingested with food Total
  • 5. Information processing in RODOS Meteorological and Release Data, Radiological Monitoring Data GIS Data, National Data Base, Scenario Data 0 Environmental Contamination of Air, Ground, and Food, Potential Doses Radiological Situation: real-time Diagnosis + Prognosis 1 Areas, Organ Doses, People affected by Countermeasures, Health Effects, Effort and Cost Countermeasures: Strategies and Consequences 2 Ranked List of feasible Strategies of long-term countermeasures (Decision Analysis) Evaluation of Strategies 3
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
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  • 17. Problem Structuring aims at hierarchically modelling the decision criteria overall goal overall objective logistics dose sub-objectives collective dose saved individual dose saved waste work effort attributes strategy y strategy x strategy z alternatives
  • 18. Web-HIPRE provides various preference elicitation methods The direct weighting dialog Preference Elicitation The “SMART” weighting method The “SWING” weighting dialog
  • 19. The communication of the results is accompanied by sensitivity analyses in Web-HIPRE The composite priorities illustrate the results of the analysis and the contributions of the different criteria to the overall results Aggregation Sensitivity Analysis Sensitivity analyses show the effect of changing the weight of an objective and give an overall assessment of the decision parameters
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.

Hinweis der Redaktion

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