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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 in nuclear response3 28.11.2017
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
Big Data in nuclear response5 28.11.2017 The JRodos Team
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
Big Data in nuclear response6 28.11.2017
Typical result of dose model - deterministic
Intervention level for
“sheltering”: 10 mSv
The JRodos Team
Big Data in nuclear response7 28.11.2017
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
Big Data in nuclear response8 28.11.2017
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?
Big Data in nuclear response9 28.11.2017
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
Big Data in nuclear response10 28.11.2017
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
Big Data in nuclear response11 28.11.2017
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
Big Data in nuclear response12 28.11.2017
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
Big Data in nuclear response13 28.11.2017
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.
Big Data in nuclear response14 28.11.2017
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
Big Data in nuclear response15 28.11.2017
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)
Big Data in nuclear response16 28.11.2017
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.
Big Data in nuclear response17 28.11.2017
Retrieval step
The JRodos Team
Illustration of working principle of CBR based on Leake (1996) Case-based reasoning: Experiences, Lessons, and Future Directions.
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
Problem description space
Solution space
New problem
?
Big Data in nuclear response18 28.11.2017 The JRodos Team
Problem description space
Solution space
?
Retrieve similar cases
Fixed number
Cases whose similarity to the
query exceed a certain
threshold
New problem
Illustration of working principle of CBR based on Leake (1996) Case-based reasoning: Experiences, Lessons, and Future Directions.
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
Big Data in nuclear response19 28.11.2017
Reuse step
The JRodos Team
Problem description space
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
Illustration of working principle of CBR based on Leake (1996) Case-based reasoning: Experiences, Lessons, and Future Directions.
Big Data in nuclear response24 28.11.2017
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
Big Data in nuclear response25 28.11.2017
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.
Big Data in nuclear response26 28.11.2017
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
Big Data in nuclear response27 28.11.2017
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)
Big Data in nuclear response28 28.11.2017
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
Big Data in nuclear response29 28.11.2017
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