bayesian(workshop( · presentation 1. objectives from dow 2. from hazards to impacts 3. framework...

This project has received funding from the European Union’s Seventh Programme for Research, Technological Development and Demostra>on under Grant Agreement No. 603458. This presenta>on reflects the views only of the authors, and the European Union cannot be considered liable for any use that may be made of the informa>on contained therein.

Bayesian Workshop

Bayesian Workshop, Berlin, 16. June 2015

Milestone 9: First training session to set-‐up the DSS for the case study sites

Wiebke Jäger

Presentation1. Objectives from DOW2. From Hazards to Impacts3. Framework & Variable Categories4. Exploring the demo Decision Support Tool (interactive)5. Setting up the Structure at Site

Coffee break

Hands on: Setting up the DSS Structure at Sites

Presentation cont.6. Data Process in Delft-FEWS

Presentation (Annelies)

Program


ObjecJves Task 3.3

1.  Linking coastal hazards to socio-‐economic, environmental and cultural proper>es of receptors and to poten>al impacts

2.  Opera>onal EWS and ex-‐ante planning tool


From Hazards to Impacts


From Hazards to Impacts

mean(i1, i2, i3, i4)

mean(i1, i2, i3)


From Hazards to Impacts Vulnerability curve rail tracks

Vulnerability curve residential buildings


Framework


Variable Categories – Receptors

Examples:

•  Characterize the spa>al distribu>on of receptors •  One variable for each receptor type


Variable Categories – Impacts

Examples:

•  Calculated for single receptor not for area


Variable Categories – Hazards

Examples:

•  Calculated for single receptor not for area •  Are input for vulnerability rela>onships


Examples:

Variable Categories – Hazard BCs

•  Characterize the magnitude of storm •  Can be inferred from seaward model boundary

condi>ons


Variable Categories – Measures

Examples:

Type II: Reduce the vulnerability of receptor

à Modified vulnerability rela>onships

Type I: Reduce hazard

à Run addi>onal simula>ons

Type III: Reduce exposure of receptor

à Modified receptor distribu>on


Demo DSS


Demo DSS -‐ extended


Demo DSS -‐ extended

h^ps://publicwiki.deltares.nl/display/RISCKIT/Workshop/Berlin_Demo_DSS_III.neta


SeUng up the BN structure at site

<receptor1>.csv <receptor2>.csv … vulnerabiliJes.json

BNstructure.json

Simula>on output

Bayesian Model Adaptor

<hotspotname>.dne

Inputs Bayesian DSS



Step 1: Define BC nodes

Step 2: Define R nodes

Step 3: Define H + I nodes

Step 4: Define M nodes

Adjust (if necessary)




•  BNstructure.json •  General adaptor (GA)

•  hazardbc.nc / hazardbc_<measure><bin>.nc




•  <receptor>.csv •  BNstructure.json


Step 2: Define R nodes Making <receptor>.csv files


Index of grid points in your .nc files

Step 2: Define R nodes Making <receptor>.csv files

Pre-‐defined Areas ID of <receptor>


Step 2: Define R nodes BNstructure.json & <receptor>.csv

BNstructure.json ResBuildings.csv


Step 2: Define R nodes BNstructure.json & node in DSS

BNstructure.json


Step 3: Define H + C nodes


•  BNstructure.json •  vulnerabili>es.json •  General adaptor (GA)

•  hazard.nc Bayesian Workshop, Berlin, 16. June 2015

Depth damage curve: BNstructure.json:



BNstructure.json: Depth damage curve:



BNstructure.json:



vulnerabiliJes.json: Depth damage curve:







Adjust (if necessary)

•  BNstructure.json •  vulnerabili>es.json


BNstructure.json:



vulnerabiliJes.json:

FloodProofing = No

BNstructure.json:

FloodProofing = Yes



Task: Create BNstructure.json for the DSS for your hot spot area & upload to wiki under BNstructure_<hotspotname>.json For reference: •  Berlin_demo_DSS_III.neta •  BNstructure.json in model.zip

MeeJng, Venue, Date

Hands -‐ on

Data Process in Del^-‐FEWS Folder Structure in FEWS


Data Process in Del^-‐FEWS IniJal set up (responsibility of CSPs)


Data Process in Del^-‐FEWS Model Adaptors: 1.  General Adaptor (GA) to be made by CSPs

calls:

2.  Bayesian Adaptor (BA) made by TU Deln


Data Process in Del^-‐FEWS A^er first simulaJon finishes… … GA writes process model output to input

For type I measures only


Data Process in Del^-‐FEWS A^er first simulaJon finishes… … BA copies input files to trainingData … BA reads all files in model & trainingData … BA creates output


Data Process in Del^-‐FEWS A^er first simulaJon finishes… … BA copies input files to trainingData … BA reads all files in model & trainingData … BA creates output

Bayesian DSS (open with Ne>ca)


Data Process in Del^-‐FEWS A^er Nth simulaJon finishes… … BA copies input files to trainingData … BA reads all files in model & trainingData … BA creates output


Data Process in Del^-‐FEWS BNstructure.json &

hazardbc_<measure>_<bin>.nc

hazardbc_FloodWall_2.nc

hazardbc_FloodWall_1.nc = hazardbc.nc


Data Process in Del^-‐FEWS BNstructure.json & hazardbc.nc

& hazard.nc

I in hazard.nc

Zs, Hs in hazardbc.nc ResBuildings.csv


bayesian(workshop( · presentation 1. objectives from dow 2. from hazards to impacts 3. framework...

Documents