A BAYESIAN TAKE ON SERRAStefano Balbi (BC·3 – Ca’ Foscari University)Ferdinando Villa (BC·3)

ZURICH - SIHL

Venice, September 19th 2013

Background

¨ This work is an application of the SERRA (Socio-Economic Regional Risk Assessment) methodology (V Mojtahed, C Giupponi, C Biscaro, AK Gain, S Balbi 2013)

¤ following up to the methodological basis outlined in a poster presented at EGU Leonardo Torino 2012 (S Balbi, V Mojtahed, C Giupponi 2012)

¤ modelling flood damage to people through the use of Bayesian Networks (BN)

¤ building a spatially distributed BN model: the same BN applies to all the cells of a grid and produces different outcomes according to varying conditions

Functions

¨ Hazard = f(Depth, Velocity, Debris Factor)¨ Vulnerability = f( Coping Ability, Susceptibility,

Early Warning System, Risk Governance) ¤ See next slide figure1 for details

¨ Exposure:1. Population density2. People in Hot Spots /Public Buildings3. Bicycles + pedestrians

Bayesian Networks

Bayesian Networks

Bayesian Networks

Expert Elicitation

¨ According to various combinations of H and V experts were asked the expected outcomes in terms of one individual every 100 being affected by:1. Material damage à linked to per capita income2. Injuries3. Post traumatic stress disorder4. Death

¨ R = H x V is the meta-model that mediates the knowledge of experts

¨ Upper level models define:¤ Hazard, Vulnerability and Exposure

Expert Elicitation

¨ According to various combinations of H and V experts were asked the expected outcomes in terms of one individual every 100 being affected by:1. Material damage à linked to per capita income2. Injuries3. Post traumatic stress disorder4. Death

¨ R = H x V is the meta-model that mediates the knowledge of experts

¨ Upper level models define:¤ Hazard, Vulnerability and Exposure

Network Learning

¨ The networks contain probability distributions¨ The probability distributions are based on the

evidences given by the experts¨ This automated procedure is called learning and it

is meant to train the networks so that they can reproduce the opinion of the experts

Exposure

¨ In the questionnaire experts where also asked to define the effect of exposure (E) on risk (R).¤ Preliminary results are produced under the assumption

that R increases/decreases proportionally with E

1. Under the scenario “flood night hit” the probabilities are applied to the residential population (à Preliminary results)

2. Under the scenario “flood day hit” the probabilities are applied to the residential and the transient population

Preliminary Results

¨ 300 years return period¨ Flood night hit (only damage to residential people)¨ Probability of 1 every 100 individuals of being

affected by…¨ 5 maps:1. Material damage2. Injury3. Posttraumatic Stress Disorder (PTSD)4. Uncertainty of results on PTSD (coefficient of variation)5. Death

Preliminary Results

¨ 300 years return period¨ Flood night hit (only damage to residential people)¨ Probability of 1 every 100 individuals of being

affected by…¨ 5 maps:1. Material damage2. Injury3. Posttraumatic Stress Disorder (PTSD)4. Uncertainty of results on PTSD (coefficient of variation)5. Death

Preliminary Results

¨ 300 years return period¨ Flood night hit (only damage to residential people)¨ Probability of 1 every 100 individuals of being

affected by…¨ 5 maps:1. Material damage2. Injury3. Posttraumatic Stress Disorder (PTSD)4. Uncertainty of results on PTSD (coefficient of variation)5. Death

Added value of this methodology

¨ Integrates subjective and local knowledge: the probabilities are based on expert elicitation.

¨ Probabilistic results (vs deterministic equations): results are expressed in both crisp values and their probability distribution

¨ Includes analysis of uncertainty

Added value of this implementation

¨ Comparative analysis with alternative scenario based on the improvement of the Early Warning System.

¨ Takes into account transient people (hot spots, public buildings), more properly considering a flood hitting during the day, when people is not located in their houses.

Next Steps

¨ Include transient people (flood day hit scenario)¨ Analyze effects on the effectiveness of EWS ( btw.

baseline based on 4 local experts interview)¨ The economic analysis:¤ Link material damage to per capita income¤ Link injuries and PTSD to values (e.g. disability adjusted

life years)¤ Link loss of lives to values (e.g. value of statistical life)

¨ Simulate different hazards with weather generator¨ …

Final Notes

¨ 25 experts involved

¨ 20 with more than 5 years experience on floods¨ 15 have been consulted by public bodies on floods¨ 10 with direct knowledge about the case study

¨ Cross-validation:¤ Preliminary results coherent with RRA-people

¨ The modelling platform used is ThinkLab, a semantically-based continuation of ARIES, developed by Ferdinando Villa (BC·3)

¨ Special thanks to Tessa Hegetschweiler (WSL) for the assistance on data retrieval and processing