nuclear emergency response and big data technologies

KIT – The Research University in the Helmholtz Association

Institute for Nuclear and Energy Technologies

www.kit.edu

Nuclear emergency response and Big Data

technologies

Wolfgang Raskob and Stella Möhrle

Karlsruhe Institute of Technology (KIT)

Big Data in nuclear response2 28.11.2017

Outline

Big Data & nuclear emergency management

Real time systems & JRODOS

Uncertainty issues & current research

Possibilities for applying Big Data technologies

Presentation of a web-based decision support system

Application example

The JRodos Team


Big Data & nuclear emergency management

Big data: how we understand it

Typically unstructured or semi-structured information available at

different locations and in different formats

Objective is to find a way to use it for a particular purpose

In nuclear e.g. for decision making in case of nuclear or radiological

events such as Chernobyl or Fukushima

Typical issues in emergency management

Information is sparse and incomplete

Information is uncertain

Information changes with time

Decisions should be taken as early as possible to save doses, e.g. if

evacuation is completed before the cloud arrives the location, the dose

saved is maximal

There is information out from previous disasters, exercises and

applications: we should use it!

The JRodos Team

Big Data in nuclear response4 28.11.2017 The JRodos Team

Areas and people affected, doses, health effects, effort, costs

(measurements)

Radiological monitoring data

database

Feasible

strategies

for longer

term

actions

evaluation

of counter-

measure

strategies

simulation

of counter-

measures

and con-

sequences

simulation

of

radiological

situation

Environmental

contamination of

air, ground, and

food, potential

doses

(measurements, forecasts)

Meteorological data, release data

Current approach: real-time systems


Transition

phaseLong-term post-accident phase

Hours/days Days/weeks/months Weeks/months/years/decades

Release

phase

Pre-release

phase

Late phaseEarly phase

Radiological situation; early countermeasures; reduction of contamination

JRODOS "Emergency" chain models

(atmospheric dispersion, early actions, food chain)

ERMIN

European model for inhabited areas (decontamination,

relocation)

AgriCP

Countermeasures in agricultural areas

JRODOS modules relevant for different phases


Typical result of dose model - deterministic

Intervention level for

“sheltering”: 10 mSv

The JRodos Team


Input data (two key variables):

Source term variations of several orders of magnitude are possible

Weather

Approach: Use ensembles to reduce uncertainty

Set of deterministic results based on slightly changed input and model

parameters

Main source of uncertainty & current research


Possible visualization of the ensemble result

The JRodos Team

Areas indicating the probabilities for

exceeding the dose threshold when

sheltering is applied.

Problem: Decision makers have to

decide which area/probability is

appropriate?


Existing scenarios

Following Fukushima, Germany revised the pre-planning for early phase

countermeasures such as evacuation, sheltering, and distribution of iodine

tablets

Weather data from the German weather service covering one

representative year (Nov. 2011 to Oct. 2012)

Every day, a calculation was performed for different source terms and

sites (> 5000 calculations)

Results: Estimation of areas and distances based on the above mentioned

countermeasures

The JRodos Team

RODOS BeispielgrafikMax distanceRODOS Beispielgrafik

Affected sectors Affected area


Possibilities for applying Big Data technologies

Besides the calculated scenarios even more can be developed in the future.

With regard to the ensemble approach, source term and weather variations

may lead to a large data source for analysis.

Question: Is there a possibility to use these data for decision making to

overcome the uncertainty issue in the early phase?

Are there possibilities to support communicating probabilities?

Which information may be additionally useful for this purpose?

How to make the obtained results accessible?

Information on historical events is available as well. How can we integrate

it?

The JRodos Team

Web-based decision support tool for all phases

of a nuclear accident


Realization in the EC project PREPARE

Objective: IT-based support for information collection and processing under

high uncertainty as basis for decision making

The following steps were performed

Development of a knowledge database

Implementation of Case-based reasoning (CBR)

Selection of retrieval criteria from the knowledge database

Definition of similarity functions and adaptation mechanisms

Development of a user interface

Demonstration with limited database in real-time

The system is available as part of a so called “Analytical Platform” at KIT and

at present the NERIS community (organisations interested in nuclear and

radiological emergency management and rehabilitation preparedness)

discusses the usage of the tool

The JRodos Team


Knowledge database

Assumption: Dividing the overall problem into sub-problems

Overall problem is to find appropriate strategies in case of a (potential)

release with the aim to protect public and environment

Sub-problem refers to specific countermeasure strategies implemented in

a certain area during a specific accident phase case

Approach:

Gathering potentially decisive attributes from the nuclear and non-nuclear

field

Experts voted on attributes concerning their importance for a specific

accident phase

Analysis of pre-defined value ranges for symbolic attributes

Structuring attributes for establishing a database schema

The JRodos Team


Excerpt of the database schema

The JRodos Team

Moehrle & Raskob (2015) Structuring and reusing knowledge from historical events for supporting nuclear emergency and remediation management. Engineering Applications of

Artificial Intelligence 46, 303-311.


Content of the knowledge database

The JRodos Team

9%

85%

6%

Historical events Scenarios Rules

6%

66%

11%

17%

Pre-release phase

Release phase

Transition phase

Long-term post-accident phase


Case-based reasoning for decision support

The JRodos Team

CBR cycle based on Aamodt & Plaza (1994) Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Communications 7(1), 39–52.

Objective: Provide suggestions on possible management options

Identify similar cases from the database

Reuse their solutions (applied or applicable strategies)


Retrieval step

The JRodos Team

Problem description space

Solution space

New problem

?

Select candidates for the nearest

neighbours

Filter attributes: accident phase

and user-specified attributes

Illustration of working principle of CBR based on Leake (1996) Case-based reasoning: Experiences, Lessons, and Future Directions.


Retrieval step

The JRodos Team


𝑓𝐺 𝑑𝑞, 𝑑𝑐 = 𝜎 𝑓𝑖 𝑞𝑖 , 𝑎𝑖 , 𝑤𝑁 𝑖 ∈ 𝑁 , 𝑐 ∈ 𝐶𝐵𝐹

Calculate similarity values

Choice of attributes and

weights are user-specified

Global similarity Local similarities

Aggregation function Weight vector

Filtered problem space


Solution space

New problem

?



Solution space

?

Retrieve similar cases

Fixed number

Cases whose similarity to the

query exceed a certain

threshold

New problem


Retrieval step

𝑓𝐺 𝑑𝑞, 𝑑𝑐 = 𝜎 𝑓𝑖 𝑞𝑖 , 𝑎𝑖 , 𝑤𝑁 𝑖 ∈ 𝑁 , 𝑐 ∈ 𝐶𝐵𝐹

Calculate similarity values

Choice of attributes and

weights are user-specified

Global similarity Local similarities

Aggregation function Weight vector

Filtered problem space


Reuse step

The JRodos Team


Solution space

Merging and adaptation to transform

solutions of most similar cases into a

solution that fits new circumstances

Merging to cover wide range of

query targets

Adaptation of area sizes, number

of affected people, costs, and

waste

New problem



Components of the Platform

The JRodos Team


Demonstrator of the Analytical Platform

The JRodos Team


Input mask of CBR tool

The JRodos Team


Result display of CBR tool

The JRodos Team


Application example of CBR tool

The JRodos Team

Timeline for Units 1, 2, and 3

Classification Organization

CBR 1st run

earthquake damage

assessment

Nuclear

Emergency (19:03

JST) Japanese Government

CBR 2nd run prerelease phase

INES 4 (Unit 1)

Nuclear and Industrial Safety

Agency, NISACBR 3rd run release phase

INES 7 Experts

12 M

arc

h 2

011

14:46

15:27

20:50

23:50

11 M

arc

h 2

011

Events Management options Accident phase

14:00

15:36Explosion at

unit 1

Venting at unit 1

Earthquake

Tsunami

Evacuation in a 2 km radius

around the plant

primary

containment

vessel of unit 1

exceeds max.

design pressure Extension of

the

evacuation

area to a 20

km radius

Official and possible event classificationPossible use of

CBR


CBR 1st run - earthquake parameters

The JRodos Team

CBR 1st run

Earthquake Weight

Magnitude 9 5

Magnitude type Mw 5

Depth 25 km 2

HDI 0,891 8

Location Japan Equal

Number of similar events 10

Result Similar events determined were Earthquake in Valdivia (Chile), which triggered a tsunami that affected the whole pacific region)

and other events in Japan, but with much less casualites.


Application example of CBR tool

The JRodos Team

Timeline for Units 1, 2, and 3

Classification Organization

CBR 1st run

earthquake damage

assessment

Nuclear

Emergency (19:03

JST) Japanese Government

CBR 2nd run prerelease phase

INES 4 (Unit 1)

Nuclear and Industrial Safety

Agency, NISACBR 3rd run release phase

INES 7 Experts

12 M

arc

h 2

011

14:46

15:27

20:50

23:50

11 M

arc

h 2

011

Events Management options Accident phase

14:00

15:36Explosion at

unit 1

Venting at unit 1

Earthquake

Tsunami

Evacuation in a 2 km radius

around the plant

primary

containment

vessel of unit 1

exceeds max.

design pressure Extension of

the

evacuation

area to a 20

km radius

Official and possible event classificationPossible use of

CBR


CBR 2nd run – nuclear accident parameters

The JRodos Team

CBR2nd run

Prerelease

nuclearEvent tab

name Fukushima Demo

begin 11 March 2011 14:46

accident type nuclear power plant accident

event description Earthquake and Tsunami on the east coast of Japan. Four power plants are threatened. Sever accident may happen.

npp tab

name Fukushima Daiichi

npp type boiling water reactor

themal power 2812

affectedArea tab

name Fukushima

areaType prefecture

population distribution rural

prerelease tab

Risk of core melt yes or unknown

maintaining of containment integrity yes or unknown

wind direction variable or unknown

estimated release time before evacuation withing 5 km radius

Result Sheltering and stable iodine tablets up to 20 km in a zone of 360 degrees (Herca Wenra)


Application of the Analytical Platform

Possibilities

Use of existing information that has been prepared from different studies

or exercises

Quick reaction even with limited information

Collect information at one place

Use of uncertain information

Help interpreting accident outside the own country

The knowledge database contains information from many historical events

and scenarios and can be used to train decision makers – which

decisions are good in particular events

Challenges

Structuring of the information, e.g. sort particular meteorological events

into attribute categories (dry, wet, turbulence status etc.)

Agree on particular countermeasure strategies for each of the

scenarios as this can be the basis for better decision making

The JRodos Team


Conclusions and possible future activities

Several thousands of scenario calculations are available and now they have

to be characterized for the knowledge database

Develop mechanisms allowing to perform such a task for any possible nuclear

power plant to create a comprehensive database for such a purpose

Investigate cascading effects of natural hazards such as floods, earthquakes

and storms that my cause nuclear accidents

Additional damages

Resources needed for both events

Casualties and priorities in response

Investigate to add a “Twitter” web crawler to initiate the start of the Analytical

Platform

Realized in the frame of CEDIM activities at KIT for natural disasters

Discuss with potential end users about the applicability of the Analytical

Platform and how this fits into their operational approaches in emergency

management

The JRodos Team


Thank you very much for

your attention

Questions?

https://resy5.iket.kit.edu/