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Amin Rasekh Kelly Brumbelow Emily Zechman May 19, 2010

World Environmental and Water Resources Congress 2010. WDS Vulnerability Analysis : Focusing on Random Factors, Consumer Behavior, and System Dynamics in Contamination Events. Amin Rasekh Kelly Brumbelow Emily Zechman May 19, 2010. Research Issues.

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Amin Rasekh Kelly Brumbelow Emily Zechman May 19, 2010

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  1. World Environmental and Water Resources Congress 2010 WDS Vulnerability Analysis:Focusing on Random Factors, Consumer Behavior, and System Dynamics in Contamination Events AminRasekh Kelly Brumbelow Emily Zechman May 19, 2010

  2. Research Issues • Human behavior in WDS contamination relatively unknown • WDS are complex, dynamic, and uncertain • Managers receive fragmented and indistinct information • Manager actions can have unintended consequences

  3. A New Response Planning Process

  4. Vulnerability Assessment ?

  5. Case Study: Mesopolis Airport & Industry University Campus Suburbs – Residential & Commercial Old City – Commercial & High-Density Low Density Residential Suburbs – Residential & Commercial Low Density Residential Naval Base ~ 8 miles

  6. Case Study: Mesopolis Low Density Residential Low Density Residential University Naval Base Suburbs Old City Suburbs East Plant West WTP West Plant East WTP

  7. Meta-Analysis of Past Events (~80 events, Hrudey and Hrudey 2004) Occurrence Probability Estimated Infection Cases

  8. Meta-Analysis & Stochastic Characterization Average number of doses per event scaled to Mesopolis population (Millions) Number of Organisms Frequency Distribution (C jejuni)

  9. Meta-Analysis & Stochastic Characterization Contamination Occurrence Location Demand Multiplier Distribution (New York City, Angelos 2000)

  10. Monte Carlo Simulation Process Public Surveys Agent-based Model Stochastic Inputs ContaminantType Contaminant Quantity Duration of Introduction Source Location Demand Multiplier Simulation Model

  11. Monte Carlo Simulation Results

  12. Optimization

  13. Optimization Results: Pathogens Exposure × Probability Risk

  14. Optimization Results: Plants Exposure × Probability Risk

  15. Optimization vs.Simulation 0 Optimization Monte Carlo Simulation West Plant (majority) East Plant (only)

  16. Optimization & Simulation 0 Optimization Monte Carlo Simulation West Plant (majority) East Plant (only)

  17. Future Work • Sensitivity analysis for current stochastic inputs and ingestion models • Integration of consumer agents into this framework • Meta-analysis of utility management to develop manager agents: information flow, false positives, delay in response • Multi-objective optimization of response plans for developed scenarios

  18. Acknowledgment National Science Foundation Infrastructure Management & Hazards Response Program

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