World Environmental and Water Resources Congress 2010
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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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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


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


A New Response Planning Process


  • Vulnerability Assessment

?


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


Case Study: Mesopolis

Low Density Residential

Low Density Residential

University

Naval Base

Suburbs

Old City

Suburbs

East Plant

West WTP

West Plant

East WTP


Meta-Analysis of Past Events (~80 events, Hrudey and Hrudey 2004)

Occurrence Probability

Estimated Infection Cases


Meta-Analysis & Stochastic Characterization

Average number of doses per event scaled to Mesopolis population (Millions)

Number of Organisms Frequency Distribution (C jejuni)


Meta-Analysis & Stochastic Characterization

Contamination Occurrence Location

Demand Multiplier Distribution

(New York City, Angelos 2000)


Monte Carlo Simulation Process

Public Surveys

Agent-based Model

Stochastic Inputs

ContaminantType

Contaminant Quantity

Duration of Introduction

Source Location

Demand Multiplier

Simulation Model


Monte Carlo Simulation Results


Optimization


Optimization Results: Pathogens

Exposure × Probability

Risk


Optimization Results: Plants

Exposure × Probability

Risk


Optimization vs.Simulation

0

Optimization

Monte Carlo Simulation

West Plant (majority)

East Plant (only)


Optimization & Simulation

0

Optimization

Monte Carlo Simulation

West Plant (majority)

East Plant (only)


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


Acknowledgment

National Science Foundation

Infrastructure Management & Hazards Response Program


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