1. Finnish Case Study: Bayesian Network Modelling
Workshop
FMI Helsinki
16th February 2017
Dr. Julie Clarke
Roughan & O’Donovan Innovative Solutions
This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no The contents of this presentation are the author's views. The European Union is not liable for any use that may be made of the information contained therein.

2. Finnish Case Study
Implementation of RAIN risk-based decision making framework
Assess the societal, security and economic impacts of critical infrastructure failure
Study area: 13 municipalities in the region of Uusimaa

3. Focus on four types of Critical Infrastructure:
Finnish Case Study
Focus on four types of Critical Infrastructure:
Road Infrastructure

4. Focus on four types of Critical Infrastructure:
Finnish Case Study
Focus on four types of Critical Infrastructure:
Rail Infrastructure

5. Focus on four types of Critical Infrastructure:
Finnish Case Study
Focus on four types of Critical Infrastructure:
Energy Infrastructure

6. Focus on four types of Critical Infrastructure:
Finnish Case Study
Focus on four types of Critical Infrastructure:
Telecommunications Infrastructure

7. Risks
Quantify the impacts of the failures and the quantifiable benefits from a societal, security and economic standpoint of providing resilient infrastructure.

8. January 2005, Storm Surge Event
Coastal flooding
Estimated damage of €20 million in costs to SAMPO insurance company

9. Bayesian Network (BN) Modelling
A graphical method that employs Bayesian probability theory to represent complex processes or networks
Facilitates the combination of information from various sources

10. Bayesian Network (BN) Modelling
Qualitative component
Each node represents a variable in the model.
Presence of an edge linking two variables indicates the existence statistical dependence between them.

11. Bayesian Network (BN) Modelling
Quantitative component
Necessary to define a probability distribution for each node conditional on its parents.
Used to define how strong the relationships are between the variables.

12. Bayesian Network (BN) Modelling
A graphical method that employs Bayesian probability theory to represent complex processes or networks
Facilitates the combination of information from various sources
Define nodes (random variables)
Construct BN model
Assign probabilities to each node

13. Sample BN Model

14. Sample BN Model

15. Coordinator: Mr. Peter Prak
Working Session
Afternoon 13:15 – 15:30
Coordinator: Mr. Peter Prak
Objective: To gather stakeholder input to assist in the development of a Bayesian Network (BN) model for the selected case study.
Define nodes for BN model (variables)
Define the dependence and independence relationships between the variables.
Stakeholder opinion will be used to guide the model and to focus on the most important parts.

16. Working Session Hazards Infrastructure Vulnerability Consequences
Source / effects
Cascading effects
Probabilities
Infrastructure Vulnerability
Failure modes
Inter-network effects
Consequences
Societal
Economic
Security

17. Working Session

18. RAIN Project www.rain-project.eu Julie Clarke julie.clarke@rod.ie