Raskob Iscram 2009

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Uncertainty handling within the Decision support system RODOS

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  • Raskob Iscram 2009

    1. 1. Approaches to visualisation of uncertainties to decision makers in an operational Decision Support System W. Raskob1, F. Gering2, V. Bertsch3 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 KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH) www.kit.edu
    2. 2. Outline Introduction Short description of the decision support system RODOS (Real- time On-line Decision SuppOrt system) Early phase issues Late phase issues Conclusions ISCRAM 2009; Raskob; 2
    3. 3. ISCRAM 2009; Raskob; 3
    4. 4. Exposure during and after a nuclear accident Total Inhaled from plume External from plume Dose rate External from deposition Ingested with food Ingested with water Hours Days Weeks Months Years R. Mustonen Accident happened Time ISCRAM 2009; Raskob; 4
    5. 5. Information processing in RODOS Meteorological and Release Data, Radiological Monitoring Data 0 GIS Data, National Data Base, Scenario Data Radiological Situation: Environmental Contamination 1 real-time Diagnosis + of Air, Ground, and Food, Prognosis Potential Doses Countermeasures: Areas, Organ Doses, People 2 Strategies and affected by Countermeasures, Consequences Health Effects, Effort and Cost Ranked List of feasible Evaluation Strategies of long-term 3 of Strategies countermeasures (Decision Analysis) ISCRAM 2009; Raskob; 5
    6. 6. Different types of uncertainty are of different importance in the different phases of emergency management Emergency Data Uncertainties Parameter (preferential) Uncertainties Early Phase: Uncertainty of the Input Data - Meteorological Fields - Source term Intermediate Phase: Measurement Uncertainties Late Phase: Uncertainties of CSY-Simulations and Uncertainties of decision parameters - Weights t - Value functions ISCRAM 2009; Raskob; 6
    7. 7. Issues in the early (pre-release) phase Problem How to deal with it Source term very uncertain • In plant data used to estimate source term on best information available (ASTRID, STERPS) Results from dose assessments • Improve weather forecast and are uncertain due to the very uncertain source term and simulation models uncertainties in the weather forecast (besides limitation of the dispersion model and the conversion of activity to dose) Decisions have to be taken with very uncertain input to initiate evacuation, sheltering or distribution of stable iodine ISCRAM 2009; Raskob; 7
    8. 8. Typical result of dose model Dose for action: “sheltering” is 10 mSv ISCRAM 2009; Raskob; 8
    9. 9. Comparison of on-site and prognostic weather data N e c k a r w e s th e im , 5 8 m . 360 W in d s p e e d a t 4 0 m > 3 m /s Preliminary results for a NPP in hilly terrain in Germany N W P w in d d ir e c t io n [d e g ], 0 to 1 1 h o u r s f o r e c a s t W in d s p e e d a t 4 0 m < 3 m /s Statistics of differences 270 between numerical weather forecast and Neckarwestheim data for the first 11 hours of a 180 48 hour prognosis Limited set of data (less than 3 months) 90 0 0 90 180 270 360 M e a s u r e d w in d d ir e c tio n [d e g ] ISCRAM 2009; Raskob; 9
    10. 10. Uncertainty modelling Model parameters Uncertainty of model parameters Model input Ensembles Uncertainty of model parameters model input Ensemble-Kalman filter used to generate 100 Ensembles Distribution of uncertain model parameters is derived a priori ISCRAM 2009; Raskob; 10
    11. 11. Ensemble calculations Main source of uncertainty for atmospheric dispersion modelling is the input data (two key variables): Source term: log-normal distribution is assigned to the source term since a deviation of an order of magnitude is considered to be equiprobable in both directions Wind direction: normal distribution is assigned to the mean wind direction with a standard deviation of 30° ISCRAM 2009; Raskob; 11
    12. 12. Communication of results in RODOS Two types of results considered in German RODOS Decision relevant: colour coding is: Green: no problem Yellow: be careful Reddish: level is exceeded Not decision relevant: colour code is a variety of blue Problem Colour-blindness (red-green) Printing ISCRAM 2009; Raskob; 12
    13. 13. Visualisation of uncertainties (2D) Two layers, one showing the mean value and the second the standard deviation (from http://www.cse.ohio- state.edu/~bordoloi/Pubs/pdfCluster.pdf) Weather forecast: movement of storm with trajectory and area of potential deviation from the mean trajectory (from NOAA) ISCRAM 2009; Raskob; 13
    14. 14. Proposed visualisation Proposed visualisation of the impact of data uncertainties The area and location of the probability to exceed the dose threshold for sheltering is displayed Decision makers have to decide which area is appropriate? ISCRAM 2009; Raskob; 14
    15. 15. Issues in the later phase Problem How to deal with it Many possible countermeasures • Measurements and might be applicable to reduce the countermeasure simulations by dose or consequences DSS provide basis for a decision Non quantifiable factors influence • Decisions analysing support the decision tools provide means to deal with non quantifiable factors such as social or political aspects Decisions have to be taken with relative certain input but other ‚soft‘ factors have to be taken into account ISCRAM 2009; Raskob; 15
    16. 16. Decision making in the context of emergency management Resolving conflicting objectives, setting priorities and building consensus for the various perspectives of the many stakeholder groups One has to ensure transparency during the decision making process First, the problem has to be structured and analysed and second the preferences and importance of the influencing factors have to be determined This task can be performed either as iterative process or with the help of tools (e.g. Multi Criteria Decision Analysis with Multi- Attribute Value Theory) ISCRAM 2009; Raskob; 16
    17. 17. Problem Structuring aims at hierarchically modelling the decision criteria overall objective sub-objectives attributes alternatives collective dose saved dose strategy x individual dose saved overall goal strategy y waste logistics strategy z work effort ISCRAM 2009; Raskob; 17
    18. 18. Web-HIPRE provides various preference elicitation methods Preference The direct Elicitation weighting dialog The “SWING” weighting dialog The “SMART” weighting method ISCRAM 2009; Raskob; 18
    19. 19. The communication of the results is accompanied by sensitivity analyses in Web-HIPRE The composite priorities illustrate the results Sensitivity analyses show the effect of of the analysis and the contributions of the changing the weight of an objective and give different criteria to the overall results an overall assessment of the decision parameters Sensitivity Aggregation Analysis ISCRAM 2009; Raskob; 19
    20. 20. Example data for Web-Hipre 100 ensembles from the atmospheric dispersion calculations Example Deviation from Deviation from ensembles mean wind mean source were used to assess the potential direction term countermeasures/consequences 1 ± 0° x 1.0 2 ± 0° x 0.01 No individual uncertainty analysis is 3 + 30° x 1.0 performed in the countermeasure 4 ± 0° x 100 subsystem 5 - 29° x 0.02 Preferences 6 - 40° x 0.007 7 + 6° x 5.1 8 - 24° x 0.9 9 + 48° x 488 10 - 4° x 1.2 Distribution normal log-normal ISCRAM 2009; Raskob; 20
    21. 21. Composite priorities Visualisation of three results for the composite priorities (5%, mean and 95% percentiles) Important: does the “best” option change for a given percentile ISCRAM 2009; Raskob; 21
    22. 22. Sensitivity analysis Sensitivity anlysis can be performed for all three percentiles ISCRAM 2009; Raskob; 22
    23. 23. Conclusions and future steps Uncertainties are part of any decision making in emergency situations Uncertainties are not much considered in Decision Support Systems for nuclear and radiological emergencies Time consuming calculations Decision makers prefer deterministic results (German experience) Ensemble method provides a good basis for determining uncertainty bands Visualisation in terms of probability bands is one possible outcome of such an uncertainty handling in the early phase Visualisation of distinct percentiles might be a good solution for the later phase Visualisation will be tested in future work shops and the RODOS Users Group (RUG) ISCRAM 2009; Raskob; 23
    24. 24. Thank you for your attention Questions? http://www.euranos.fzk.de ISCRAM 2009; Raskob; 24

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