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Decision Support System to Manage Critical Civil Infrastructure Systems for Disaster Resilience (Sylvana Croope, DelDOT)
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Decision Support System to Manage Critical Civil Infrastructure Systems for Disaster Resilience (Sylvana Croope, DelDOT)

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  • Elevation 8 – 30 feet
  • Step 1 – getting local infrastructure information, initializing the system
    Step 2 – getting system performance measures
    Steps 3 and 4 – degrading system performance due to a disaster
    Step 5 to 7 – improving system performance
    Step 8 – assessing performance.
  • Transcript

    • 1. Silvana V Croope, PhD Dig DUG Delaware User Group Meeting 11/03/2010
    • 2. Case Study: Delaware Flooding – June 2006 Visiting Existing Decision Support Systems Critical Infrastructure Resilience Decision Support System (CIR-DSS) Spatial Decision Support System (SDSS) SDSS: GIS + HAZUS-MH analyses SDSS Benefits and Limitations Complementary Systems for CIR-DSS Integrating SDSS analyses results to other systems Case Study results summary Conclusion
    • 3. (USGS) Federal Disaster Declarations 1965-2003: Sussex County – DE 1 FDD 2 FDD 4 or more FDD 3 FDD
    • 4. Keenan (2004) Bush et all (2007) de la Garza et all (1998)
    • 5. Objective: To improve the resilience of critical infrastructure systems Decision variables – To undertake mitigation Other variables Resilience metrics: capacity, pavement condition Net present value of costs and costs avoided
    • 6. What if scenario (e.g.)
    • 7.  Geographic: hydrology, elevation  Hazard: weather, disaster history records and trends  Infrastructure: roads, bridges  Financial: cost of infrastructure  Institutional/cultural: decision- makers, policies and funding  Infrastructure life-cycle  Expected infrastructure life-cycle with mitigation  Vulnerability, Risk/Impact and Damage Analysis  Pre-, During and Post-Event Assessment – infrastructure condition and performance  Mitigation Strategies (definition)  Pre-, During and Post-Event Resilience Assessment  Cost-Benefit Analysis  Recovery and Mitigation Strategies Comparison  Scenarios Test/Evaluation ANALYSES/ASSESSMENTS
    • 8. Decision Support Systems – CIR-DSS Macro Environment Large number of variables Focus on resilience of system problem solutions
    • 9. MAIN DATA SOURCES  DelDOT Transportation Management Center: pictures, traffic and detours reports (DelDOT Officials)  DelDOT Bridge Management: bridge data (GIS), reports of local damaged bridges, digital maps (pdf) (DelDOT Officials)  DelDOT (others): roads , State boundaries  Spat Lab (U.D.): Elevation Data  DataMIL: roads, rivers, hydrology, municipal boundaries  Delaware Environmental Observing System (DEOS): radar derived rainfall  HAZUS-MH MR3 inventory  Literature TOOLS  GIS ArcInfo and extensions  Spatial Analyst  Network Analyst  Excel  HAZUS-MH MR3  Flood Model  Analysis Level 1  STELLA®
    • 10. Vulnerability:Vulnerability: exposureexposure loss/damageloss/damage Rain Fall 06/25/2010 Study Area Elevation
    • 11. Location of Damaged Infrastructure in the Seaford Flooded Area Seaford Road Network and Detours Analysis (2006)
    • 12. Organizing principle and analytical capability
    • 13. “What if” Levee Protection Scenario “What if” Flow Regulation Scenario Floodwater Velocity Estimation Scenario Damage related to US13 in Sussex County
    • 14. Seaford Area Annual Losses Map of Depth 10-year- flood-map
    • 15. Addresses issues at specific locations – spatial data and local infrastructure Helps structure complex problems to support decision-making processes Does not replace users’ decision-making Facilitates sharing/integration of data and information on further analyses Analysis outputs are used to support decisions for infrastructure repair, improvement and mitigation of future floods
    • 16. Limitations (HAZUS-MH) •Analysis focuses on limited area •Limited 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 – T.I. resilience changes - No financial trade-off tools for solutions evaluation
    • 17. Many rich data sources to support decision making Tools HAZUS-MH MR3 Useful Limited Non-trivial Complementary GIS Helps communication Provides insight Analyses results/data integration with Management Information SystemAnalyses results/data integration with Management Information System • transportation inventory (roads – segments values)transportation inventory (roads – segments values) • transportation exposure/vulnerability valuetransportation exposure/vulnerability value • warning system value (10%)warning system value (10%) • connectivity mitigation insightconnectivity mitigation insight
    • 18. GISGIS HAZUS-MHHAZUS-MH+
    • 19. STELLA Software: system dynamics - way to represent complex problems sequence of events, relationship among infrastructure and organizations, types of policies that enables certain actions STELLA tool for modeling and simulation STELLA = skills + language + representations +STELLA = skills + language + representations + programming = thinking + communicating +programming = thinking + communicating + learninglearning
    • 20. Eight scenarios assessing impacts (costs) uses: Infrastructure Projects  Recovery only  Recovery and Mitigation Probability of a 100-year storm event in the case study area 1% 4% 8% Time required for disaster response  2 days  4 days Scenarios Explored
    • 21. 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 1% probability of 100-year storm (study area)
    • 22. • Complex system modeling required many assumptions and models to capture changes over time • SDSS plays major role in setting the stage for problem analysis, diagnosis and mitigation insights using data from many sources • SDSS results are important inputs into the model • SDSS can be better customized to include better analysis tools for infrastructure • Comprehensive development offers insights into trade-offs and opportunities to capture damage and costs in the context of resiliency • Refinements are needed to operationalize this approach
    • 23. DelDOT (supporter/employer) University of Delaware University Transportation Center (support for research) Sue McNeil (adviser)