raskob iscram 2009

KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH) www.kit.edu

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, Germany2 Federal Office for Radiation Protection, Neuherberg, Germany 3 Karlsruhe Institute of Technology , Karlsruhe, Germany

ISCRAM 2009, 10.-13-05.2009

ISCRAM 2009; Raskob; 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


Exposure during and after a nuclear accident

Ingested with water

Dos

e ra

te

Time

Inhaled from plume

External from plume

External from deposition

Ingested with food

Accident happened

Hours Days Weeks Months Years

Total

R. Mustonen


Information processing in RODOS

Meteorological and Release Data, Radiological Monitoring Data

GIS Data, National Data Base, Scenario Data0

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 Strategies3


Data Uncertainties Parameter (preferential) Uncertainties

t

Early Phase: Uncertainty of the Input Data

- Meteorological Fields

- Source term

Late Phase: Uncertainties of CSY-Simulations and Uncertainties of decision parameters

- Weights

- Value functions

Emergency

Intermediate Phase: Measurement Uncertainties

Different types of uncertainty are of different importance in the different phases of emergency management


Issues in the early (pre-release) phase

ProblemSource term very uncertain

Results from dose assessments are uncertain due to the very uncertain source term and uncertainties in the weather forecast (besides limitation of the dispersion model and the conversion of activity to dose)

How to deal with itIn plant data used to estimate source term on best information available (ASTRID, STERPS)

Improve weather forecast and simulation models

Decisions have to be taken with very uncertain input to initiate evacuation, sheltering or distribution of stable iodine


Typical result of dose model

Dose for action: “sheltering” is 10 mSv


Comparison of on-site and prognostic weather data

Preliminary results for a NPP in hilly terrain in Germany

Statistics of differences between numerical weather forecast and Neckarwestheim data for the first 11 hours of a 48 hour prognosis

Limited set of data (less than 3 months)

0 90 180 270 360

M easured w ind d irection [deg]

0

90

180

270

360

NW

P w

ind

dir

ect

ion

[de

g],

0 t

o 1

1 h

ou

rs f

ore

cast

W ind speed a t 40 m > 3 m /s

W ind speed a t 40 m < 3 m /s

N eckarw estheim , 58 m .


Model parameters

Model input

Uncertainty of model parameters

Uncertainty of model input

Ensembles

model parameters

Uncertainty modelling

Ensemble-Kalman filter used to generate 100 Ensembles

Distribution of uncertain model parameters is derived a priori


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°


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

ProblemColour-blindness (red-green)

Printing


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)


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?


Issues in the later phase

Problem

Many possible countermeasures might be applicable to reduce the dose or consequences

Non quantifiable factors influence the decision

How to deal with it

Measurements and countermeasure simulations by DSS provide basis for a decision

Decisions analysing support 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


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)

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


The direct weighting dialog

The “SMART” weighting method

The “SWING” weighting dialog

Web-HIPRE provides various preference elicitation methods

Preference Elicitation


The composite priorities illustrate the results of the analysis and the contributions of the

different criteria to the overall results

Aggregation

The communication of the results is accompanied by sensitivity analyses in Web-HIPRE

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

Example ensembles

Deviation from mean wind

direction

Deviation from mean source

term

1 0° x 1.0

2 0° x 0.01

3 + 30° x 1.0

4 0° x 100

5 - 29° x 0.02

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

100 ensembles from the atmospheric dispersion calculations were used to assess the potential countermeasures/consequences

No individual uncertainty analysis is performed in the countermeasure subsystem

Preferences


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


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)


Thank you for your attention

Questions?

http://www.euranos.fzk.de

raskob iscram 2009

Technology

d s p e e d

s u r e d w

d d ir e c tio n d e

d d ir e c t io n d

r w e s th e im

impact of data uncertainties

prognostic weather data

gis data