2. ïCase Study: Delaware Flooding â June 2006
ïVisiting Existing Decision Support Systems
ïCritical Infrastructure Resilience Decision
Support System (CIR-DSS)
ïSpatial Decision Support System (SDSS)
ïSDSS: GIS + HAZUS-MH analyses
ïSDSS Benefits and Limitations
ïComplementary Systems for CIR-DSS
ïIntegrating SDSS analyses results to other systems
ïCase Study results summary
ïConclusion
5. ïObjective: To improve the resilience of critical
infrastructure systems
ïDecision variables â To undertake mitigation
ïOther variables
ïResilience metrics: capacity, pavement condition
ïNet present value of costs and costs avoided
16. ïAddresses issues at specific locations â spatial data and local
infrastructure
ïHelps structure complex problems to support decision-making
processes
ïDoes not replace usersâ decision-making
ïFacilitates sharing/integration of data and information on
further analyses
ïAnalysis outputs are used to support decisions for
infrastructure repair, improvement and mitigation of future
floods
17. Limitations (HAZUS-MH)
âąAnalysis focuses on limited area
âąLimited tools to analyze
transportation infrastructure
(T.I.)
- Road segmentation not official
- Vehicle exposure does not consider
flow, just Census
- Exposure same as vulnerability?
- No dynamic modeling â T.I.
resilience changes
- No financial trade-off tools for
solutions evaluation
18. ïMany rich data sources to support decision making
ïTools
HAZUS-MH MR3
ïUseful
ïLimited
ïNon-trivial
ïComplementary
GIS
ïHelps communication
ïProvides insight
Analyses results/data integration with Management Information SystemAnalyses results/data integration with Management Information System
âą transportation inventory (roads â segments values)transportation inventory (roads â segments values)
âą transportation exposure/vulnerability valuetransportation exposure/vulnerability value
âą warning system value (10%)warning system value (10%)
âą connectivity mitigation insightconnectivity mitigation insight
20. STELLA Software:
ïsystem dynamics - way to represent complex
problems
ïsequence of events,
ïrelationship among infrastructure and organizations,
ïtypes of policies that enables certain actions
ïSTELLA tool for modeling and simulation
STELLA = skills + language + representations +STELLA = skills + language + representations +
programming = thinking + communicating +programming = thinking + communicating +
learninglearning
21. Eight scenarios assessing impacts (costs) uses:
ïInfrastructure Projects
ï Recovery only
ï Recovery and Mitigation
ïProbability of a 100-year storm event in the case study
area
1% 4% 8%
ïTime required for disaster response
ï 2 days
ï 4 days
Scenarios Explored
22. Variables 2 days Disaster
Response
4 days Disaster
Response
Recovery NPV 148,081 512,958
Mitigation NPV 157,890 600,629
Result from mitigation
investment
-9,809 -87,671
Loss of function for
recovery (benefit)
-1,463 -2,479
Loss of function for
mitigation (benefit)
719,299 1,218,060
ï1% probability of 100-year storm (study area)
23. âą Complex system modeling required many assumptions and
models to capture changes over time
âą SDSS plays major role in setting the stage for problem
analysis, diagnosis and mitigation insights using data from
many sources
âą SDSS results are important inputs into the model
âą SDSS can be better customized to include better analysis tools for
infrastructure
âą Comprehensive development offers insights into trade-offs
and opportunities to capture damage and costs in the context
of resiliency
âą Refinements are needed to operationalize this approach
Step 1 â getting local infrastructure information, initializing the system
Step 2 â getting system performance measures
Steps 3 and 4 â degrading system performance due to a disaster
Step 5 to 7 â improving system performance
Step 8 â assessing performance.