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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Amin Rasekh Kelly Brumbelow Emily Zechman May 19, 2010

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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

AminRasekh

Kelly Brumbelow

Emily Zechman

May 19, 2010


Research issues

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

A New Response Planning Process


Amin rasekh kelly brumbelow emily zechman may 19 2010

  • Vulnerability Assessment

?


Case study mesopolis

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 mesopolis1

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

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

Occurrence Probability

Estimated Infection Cases


Meta analysis stochastic characterization

Meta-Analysis & Stochastic Characterization

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

Number of Organisms Frequency Distribution (C jejuni)


Amin rasekh kelly brumbelow emily zechman may 19 2010

Meta-Analysis & Stochastic Characterization

Contamination Occurrence Location

Demand Multiplier Distribution

(New York City, Angelos 2000)


Monte carlo simulation process

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

Monte Carlo Simulation Results


Optimization

Optimization


Optimization results pathogens

Optimization Results: Pathogens

Exposure × Probability

Risk


Optimization results plants

Optimization Results: Plants

Exposure × Probability

Risk


Optimization vs simulation

Optimization vs.Simulation

0

Optimization

Monte Carlo Simulation

West Plant (majority)

East Plant (only)


Optimization simulation

Optimization & Simulation

0

Optimization

Monte Carlo Simulation

West Plant (majority)

East Plant (only)


Future work

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

Acknowledgment

National Science Foundation

Infrastructure Management & Hazards Response Program


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