6th International Disaster and Risk Conference IDRC 2016 Integrative Risk Management - Towards Resilient Cities. 28 August - 01 September 2016 in Davos, Switzerland

1. WIR SCHAFFEN WISSEN – HEUTE FÜR MORGEN
Towards the validation of a National Risk Assessment
against historical observations using a Bayesian
approach: Application to the Swiss case
Matteo Spada :: Risk Analyst :: Paul Scherrer Institute
Peter Burgherr :: Head of the TA Group :: Paul Scherrer Institute
Markus Hohl :: Wissenschaftlicher Mitarbeiter Risikogrundlagen :: Federal Office of
Civil Protection (FOCP)
IDRC 2016, Davos, Switzerland, 28 August – 01 September 2016

2. • Motivation
• Method
• The Swiss National Risk Assessment
• Application to selected hazards:
Floods
Blackouts
• Conclusions
Outline
Page 2

3. • National Risk Assessment (NRA) has received increased interest from governments, authorities
and other involved stakeholders
• NRA approach results in risk matrixes
• Likelihood and consequence in risk matrixes are mainly based upon subjective, qualitative
judgments or semi-qualitative approaches
• The validation of an NRA is of great importance to ensure that the analysis of national hazard
scenarios results in the implementation of adequate prevention and mitigation strategies.
Motivation
Page 3
FOCP (2013)

4. Method
Page 4
Collect Historical Observations
Apply a Threshold Analysis
(In order to get a complete dataset for the
consequence under interest)
Model the dataset with a Bayesian Approach for both frequency and consequences
(Bayesian analysis is a fully probabilistic approach intrinsically accounts for both epistemic and
aleatory uncertainty. This serves to assess the parameters to be used in the next steps)
For a given Hazard, for which we need to estimate the frequency and the related consequences extent:

5. A short Overview on Bayesian Analysis
Page 5
𝑝 𝜃 𝑦 =
𝐿 𝑦; 𝜃 𝑝(𝜃)
𝐿 𝑦; 𝜃 𝑝 𝜃 𝑑𝜃
𝑝 𝜃 𝑦 =
𝐿 𝑦; 𝜃 𝑝(𝜃)
𝐿 𝑦; 𝜃 𝑝 𝜃 𝑑𝜃
∝ 𝐿 𝑦; 𝜃 𝑝(𝜃)
Prior
Bayes
Theorem
Posterior
Data
Bayes Theorem:
Posterior: Conditional probability that is assigned after the relevant evidence is taken into
account. Thus, it expresses our updated knowledge about parameters after observing data
Prior: expresses what is known about the parameters before
observing the data. Prior describes epistemic uncertainty.
Likelihood Function: Likelihood describes the process giving rise to data
y in terms of unknown parameters θ. Likelihood describes aleatory
uncertainty.
Marginal Likelihood: Normalization constant in order to let the posterior
distribution to be “proper”. That is, the posterior should converge to 1.
By applying Markov
Chain Monte-Carlo
(MCMC) algorithms

6. Method
Page 6
Collect Historical Observations
Apply a Threshold Analysis
Model the dataset with a Bayesian Approach for both frequency and consequences
Build the product between the frequency and the Complementary Cumulative
Distribution Function (CCDF) of the consequence in order to get the frequency
(1/year) of a given extent of the consequence
(For the frequency the posterior distribution is considered. The CCDF is built as 1 – CDF, where the
CDF is drawn considering the posterior distributions of the parameters describing the consequence
under interest)
Calculate and compare the Probability in the next 10 years of the consequence
extent defined by the experts
(The following is used: 𝑃10 𝑦𝑒𝑎𝑟𝑠 = 1 − (1 − 𝑃1 𝑦𝑒𝑎𝑟)10
, where P1 year is estimated from the frequency
(1/year) extracted from the product frequency*CCDF)
For a given Hazard, for which we need to estimate the frequency and the related consequences extent:

7. The Swiss NRA
Page 7
• In FOCP (2013) for each hazard 3 possible scenarios are defined based on on events already happened
in Switzerland or worldwide:
• Significant
• Major
• Extreme
• Major: a scenario of great intensity. Nevertheless, considerably
more severe occurrences and courses of events are imaginable
in Switzerland.
FOCP (2013)
FOCP (2013)

8. The Swiss NRA
Page 8
• For each scenario both consequences, which are 12 subdivided into 4 damage areas (individual,
environment, economy, society), and likelihoods are assessed through a DELPHI survey among experts
• First the likelihood, which is defined as the probability in the next 10 years that a given event will
indeed materialize, has been selected among different classes.
• Second, each consequence under interest is selected among different classes
• Finally, marginal costs are estimated for each selected consequences in order to aggregate them.
Likelihood
Class
Description
Probability in the
next 10 years
(%)
LC8
On average, few events over a human lifespan in
Switzerland
> 30%
LC7
On average, one event over a human lifespan in
Switzerland
10-30%
LC6
Has occurred in Switzerland before, but possibly
already several generations in the past
3-10%
LC5
May not have occurred in Switzerland yet, but is
known to have happened in other countries
1-3%
LC4 Several known events worldwide 0.3-1%
LC3 Only few known events worldwide 0.1-0.3%
LC2
Only single known events worldwide, but also
conceivable in Switzerland
0.03-0.1%
LC1
Only single, if any, known events worldwide. Such
an occurrence is regarded as very rare even on a
global scale, but cannot be fully excluded for
Switzerland either.
< 0.03%
FOCP (2013)
Example for Fatalities, FOCP (2013)

9. Page 9
Application to Floods
FOCP (2013)

10. Data
Page 10
• LC5 (p = 1-3%): May not have occurred in Switzerland, but is known to have happened in other countries
• The Swiss dataset (Switzerland) is build up from data for Switzerland and Neighboring Countries (Italy,
France, Germany and Austria) based on the followings:
• Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) data for Switzerland from
1972-2013 (1205 events);
• Dartmouth Flood Observatory and MunichRe data worldwide from 1982-2010 (3728 events)
• Expected consequences: 25 fatalities and 10 Billion CHF economic losses
• Red line is the threshold estimated
with a MAXC (Maximum Curvature)
method
• Model:
Log-Normal for the consequences
Poisson for the frequency

11. Results: Model vs. Historical Observations
Page 11

12. • For fatalities, experts estimate a probability in the next 10 years slightly lower
than the model based on historical observations
• For economic losses, experts estimate a probability in the next 10 years
significantly larger than the model based on historical observations
Results: Experts vs. Model Likelihood
Page 12

13. Page 13
Application to Blackouts
FOCP (2013)

14. Data
Page 14
• LC8 (p = > 30%): On average few events in a Lifetime in Switzerland
• ELCOM data for Switzerland from 2010-2013 (26438 events)
• No consequence data available  Considering duration and number of affected customers
defining the Major scenario:
• Affected customers: 800000-1.5 Mio people
• Duration: 2-4 days with blackout + 2-3 days of getting back to normality
• Red line is the threshold estimated
with a MAXC (Maximum Curvature)
method
• Model:
Generalized Pareto for Duration
Log-Normal for Affected Costumers
Poisson for the frequency

15. Results: Model vs. Historical Observations
Page 15

16. Results: Experts vs. Model Likelihood
Page 16
• For affected costumers, experts estimate a probability in the next 10 years
significantly larger than the model based on historical observations in all cases
• For blackout duration, experts estimate a probability in the next 10 years
significantly larger than the model based on historical observations in all cases

17. • Fully Probabilistic approach, which accounts for both aleatory and epistemic uncertainty, is
defined in order to prove the expectation of an event based on expert judgement, commonly
used in NRA
• For the floods (P= 1-3%), with respect to the historical observation (1972-2013):
• Fatality (25) is expected at an average of 7% (4-12%)
• Economic losses (10 Billion CHF) is expected at low probability levels (~10-2%)
• For blackouts in Switzerland (P= > 30%) data for 2010-2013 were analyzed, and affected
customers and duration of blackout used as consequence indicators:
• Affected customers (800000 – 1.5 Mio people) are both expected for very low probability
levels 10-8% and 10-9%, respectively
• Duration (2-7 days) are all expected for low probability levels (10-2%)
• The NRA validation method proposed here has been apply to Dangerous Goods Transportation
and Windstorms as well (Spada et al., to be resubmitted)
• Results show that the consequences not necessarily match the likelihood defined by the
experts
• Among the validation purpose, the proposed approach could be useful as a complementary
approach to support and build more reliable NRAs.
Conclusions
Page 17

18. Page 18
Wir schaffen Wissen – heute für morgen
Thank you! Questions?
matteo.spada@psi.ch
www.psi.ch/ta/mspada