System Contamination

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Kelly Brumbelow - One of the best experts on this subject based on the ideXlab platform.

  • A dynamic simulation-optimization model for adaptive management of urban water distribution System Contamination threats
    Applied Soft Computing, 2015
    Co-Authors: Amin Rasekh, Kelly Brumbelow
    Abstract:

    Dynamic simulation is performed to model water distribution System Contamination.Dynamic optimization is used to track time-varying optimal response protocols.Dynamic models provide adaptive decision support for public health protection. Urban water distribution Systems hold a critical and strategic position in preserving public health and industrial growth. Despite the ubiquity of these urban Systems, aging infrastructure, and increased risk of terrorism, decision support models for a timely and adaptive Contamination emergency response still remain at an undeveloped stage. Emergency response is characterized as a progressive, interactive, and adaptive process that involves parallel activities of processing streaming information and executing response actions. This study develops a dynamic decision support model that adaptively simulates the time-varying emergency environment and tracks changing best health protection response measures at every stage of an emergency in real-time. Feedback mechanisms between the contaminated network, emergency managers, and consumers are incorporated in a dynamic simulation model to capture time-varying characteristics of an emergency environment. An evolutionary-computation-based dynamic optimization model is developed to adaptively identify time-dependant optimal health protection measures during an emergency. This dynamic simulation-optimization model treats perceived contaminant source attributes as time-varying parameters to account for perceived Contamination source updates as more data stream in over time. Performance of the developed dynamic decision support model is analyzed and demonstrated using a mid-size virtual city that resembles the dynamics and complexity of real-world urban Systems. This adaptive emergency response optimization model is intended to be a major component of an all-inclusive cyberinfrastructure for efficient Contamination threat management, which is currently under development.

  • A Dynamic Simulation-Optimization Model for Adaptive Management of Urban Water Distribution System Contamination Threats
    arXiv: Other Computer Science, 2014
    Co-Authors: Amin Rasekh, Kelly Brumbelow
    Abstract:

    Urban water distribution Systems hold a critical and strategic position in preserving public health and industrial growth. Despite the ubiquity of these urban Systems, aging infrastructure, and increased risk of terrorism, decision support models for a timely and adaptive Contamination emergency response still remain at an undeveloped stage. Emergency response is characterized as a progressive, interactive, and adaptive process that involves parallel activities of processing streaming information and executing response actions. This study develops a dynamic decision support model that adaptively simulates the time-varying emergency environment and tracks changing best health protection response measures at every stage of an emergency in real-time. Feedback mechanisms between the contaminated network, emergency managers, and consumers are incorporated in a dynamic simulation model to capture time-varying characteristics of an emergency environment. An evolutionary-computation-based dynamic optimization model is developed to adaptively identify time-dependant optimal health protection measures during an emergency. This dynamic simulation-optimization model treats perceived contaminant source attributes as time-varying parameters to account for perceived Contamination source updates as more data stream in over time. Performance of the developed dynamic decision support model is analyzed and demonstrated using a mid-size virtual city that resembles the dynamics and complexity of real-world urban Systems. This adaptive emergency response optimization model is intended to be a major component of an all-inclusive cyberinfrastructure for efficient Contamination threat management, which is currently under development.

  • Sociotechnical risk assessment for water distribution System Contamination threats
    Journal of Hydroinformatics, 2013
    Co-Authors: Amin Rasekh, M. Ehsan Shafiee, Emily M. Zechman, Kelly Brumbelow
    Abstract:

    Water distribution Systems (WDS) are vulnerable to contaminants, and Systematic risk assessment can provide valuable information for assisting threat management. Contamination events are sociotechnical Systems, in which the interactions among consumers and water infrastructure may generate unpredicted public health consequences. This research develops a sociotechnical risk assessment framework that simulates the dynamics of a Contamination event by coupling an agent-based modeling (ABM) framework with Monte Carlo simulation (MCS), genetic algorithm (GA) optimization, and a multi-objective GA. The ABM framework couples WDS simulation with agents to represent consumers in a virtual city. MCS is applied to estimate the uncertainty in human exposure, based on probabilistic models of event attributes. A GA approach is used to identify critical Contamination events by maximizing risk, and a multi-objective approach explores the trade-off between consequence and occurrence probabilities. Results that are obtained using the sociotechnical approach are compared with results obtained using a conventional engineering model. The sociotechnical approach removes assumptions that have been used in engineering analysis about the static, homogeneous, and stationary behaviors of consumers, and results demonstrate new insight about the impacts of these actions and interactions on the public health consequences of Contamination events.

  • probabilistic analysis and optimization to characterize critical water distribution System Contamination scenarios
    Journal of Water Resources Planning and Management, 2013
    Co-Authors: Amin Rasekh, Kelly Brumbelow
    Abstract:

    AbstractCharacterization of critical water distribution System (WDS) Contamination scenarios—defined by a set of attributes, a probability of occurrence, and a specific level of consequences—is a prerequisite for preparation of reliable and cost-effective mitigation, preparedness, and emergency response plans. This study develops Monte Carlo and risk-based optimization schemes to evaluate Contamination risk of WDSs for generation of this important class of scenarios, which are representative of the most vulnerable aspects of the System. Defining attributes of Contamination scenarios are identified as contaminant type and amount, Contamination location, start time, duration, and time of year scenario occurs. Well-documented waterborne outbreaks reported in developed nations are analyzed to empirically estimate statistical characteristics of defining attributes in accidental events. Monte Carlo simulation is conducted to determine the probability distribution of public-health consequences, aggregate conditi...

  • Adaptive Emergency Response to Water Distribution System Contamination Events
    World Environmental and Water Resources Congress 2012, 2012
    Co-Authors: Amin Rasekh, Kelly Brumbelow
    Abstract:

    Approximately 90% of the U.S. population receives water from one of 170,000 public water utilities. Despite the ubiquity of this infrastructure, its importance for public health, and increased risk of terrorism, many aspects of emergency management for water supply Systems remain at an undeveloped stage. Past efforts have mainly focused on static response which does not consider System dynamics and many changes that occur after Contamination starts. To effectively cope with Contamination threats, however, the emergency response needs to be adaptive in that it should account for the changing circumstances of Contamination and previous actions taken by managers and consumers as well as the stream of new threat information which become available as emergency proceeds. This article describes an ongoing study for development of simulation-optimization models for adaptive emergency response to a Contamination event. Dynamic optimization and event-driven programming is applied to create decision aid tools that support emergency managers in making timely and effective decisions.

Avi Ostfeld - One of the best experts on this subject based on the ideXlab platform.

Amin Rasekh - One of the best experts on this subject based on the ideXlab platform.

  • A dynamic simulation-optimization model for adaptive management of urban water distribution System Contamination threats
    Applied Soft Computing, 2015
    Co-Authors: Amin Rasekh, Kelly Brumbelow
    Abstract:

    Dynamic simulation is performed to model water distribution System Contamination.Dynamic optimization is used to track time-varying optimal response protocols.Dynamic models provide adaptive decision support for public health protection. Urban water distribution Systems hold a critical and strategic position in preserving public health and industrial growth. Despite the ubiquity of these urban Systems, aging infrastructure, and increased risk of terrorism, decision support models for a timely and adaptive Contamination emergency response still remain at an undeveloped stage. Emergency response is characterized as a progressive, interactive, and adaptive process that involves parallel activities of processing streaming information and executing response actions. This study develops a dynamic decision support model that adaptively simulates the time-varying emergency environment and tracks changing best health protection response measures at every stage of an emergency in real-time. Feedback mechanisms between the contaminated network, emergency managers, and consumers are incorporated in a dynamic simulation model to capture time-varying characteristics of an emergency environment. An evolutionary-computation-based dynamic optimization model is developed to adaptively identify time-dependant optimal health protection measures during an emergency. This dynamic simulation-optimization model treats perceived contaminant source attributes as time-varying parameters to account for perceived Contamination source updates as more data stream in over time. Performance of the developed dynamic decision support model is analyzed and demonstrated using a mid-size virtual city that resembles the dynamics and complexity of real-world urban Systems. This adaptive emergency response optimization model is intended to be a major component of an all-inclusive cyberinfrastructure for efficient Contamination threat management, which is currently under development.

  • A Dynamic Simulation-Optimization Model for Adaptive Management of Urban Water Distribution System Contamination Threats
    arXiv: Other Computer Science, 2014
    Co-Authors: Amin Rasekh, Kelly Brumbelow
    Abstract:

    Urban water distribution Systems hold a critical and strategic position in preserving public health and industrial growth. Despite the ubiquity of these urban Systems, aging infrastructure, and increased risk of terrorism, decision support models for a timely and adaptive Contamination emergency response still remain at an undeveloped stage. Emergency response is characterized as a progressive, interactive, and adaptive process that involves parallel activities of processing streaming information and executing response actions. This study develops a dynamic decision support model that adaptively simulates the time-varying emergency environment and tracks changing best health protection response measures at every stage of an emergency in real-time. Feedback mechanisms between the contaminated network, emergency managers, and consumers are incorporated in a dynamic simulation model to capture time-varying characteristics of an emergency environment. An evolutionary-computation-based dynamic optimization model is developed to adaptively identify time-dependant optimal health protection measures during an emergency. This dynamic simulation-optimization model treats perceived contaminant source attributes as time-varying parameters to account for perceived Contamination source updates as more data stream in over time. Performance of the developed dynamic decision support model is analyzed and demonstrated using a mid-size virtual city that resembles the dynamics and complexity of real-world urban Systems. This adaptive emergency response optimization model is intended to be a major component of an all-inclusive cyberinfrastructure for efficient Contamination threat management, which is currently under development.

  • Sociotechnical risk assessment for water distribution System Contamination threats
    Journal of Hydroinformatics, 2013
    Co-Authors: Amin Rasekh, M. Ehsan Shafiee, Emily M. Zechman, Kelly Brumbelow
    Abstract:

    Water distribution Systems (WDS) are vulnerable to contaminants, and Systematic risk assessment can provide valuable information for assisting threat management. Contamination events are sociotechnical Systems, in which the interactions among consumers and water infrastructure may generate unpredicted public health consequences. This research develops a sociotechnical risk assessment framework that simulates the dynamics of a Contamination event by coupling an agent-based modeling (ABM) framework with Monte Carlo simulation (MCS), genetic algorithm (GA) optimization, and a multi-objective GA. The ABM framework couples WDS simulation with agents to represent consumers in a virtual city. MCS is applied to estimate the uncertainty in human exposure, based on probabilistic models of event attributes. A GA approach is used to identify critical Contamination events by maximizing risk, and a multi-objective approach explores the trade-off between consequence and occurrence probabilities. Results that are obtained using the sociotechnical approach are compared with results obtained using a conventional engineering model. The sociotechnical approach removes assumptions that have been used in engineering analysis about the static, homogeneous, and stationary behaviors of consumers, and results demonstrate new insight about the impacts of these actions and interactions on the public health consequences of Contamination events.

  • probabilistic analysis and optimization to characterize critical water distribution System Contamination scenarios
    Journal of Water Resources Planning and Management, 2013
    Co-Authors: Amin Rasekh, Kelly Brumbelow
    Abstract:

    AbstractCharacterization of critical water distribution System (WDS) Contamination scenarios—defined by a set of attributes, a probability of occurrence, and a specific level of consequences—is a prerequisite for preparation of reliable and cost-effective mitigation, preparedness, and emergency response plans. This study develops Monte Carlo and risk-based optimization schemes to evaluate Contamination risk of WDSs for generation of this important class of scenarios, which are representative of the most vulnerable aspects of the System. Defining attributes of Contamination scenarios are identified as contaminant type and amount, Contamination location, start time, duration, and time of year scenario occurs. Well-documented waterborne outbreaks reported in developed nations are analyzed to empirically estimate statistical characteristics of defining attributes in accidental events. Monte Carlo simulation is conducted to determine the probability distribution of public-health consequences, aggregate conditi...

  • Adaptive Emergency Response to Water Distribution System Contamination Events
    World Environmental and Water Resources Congress 2012, 2012
    Co-Authors: Amin Rasekh, Kelly Brumbelow
    Abstract:

    Approximately 90% of the U.S. population receives water from one of 170,000 public water utilities. Despite the ubiquity of this infrastructure, its importance for public health, and increased risk of terrorism, many aspects of emergency management for water supply Systems remain at an undeveloped stage. Past efforts have mainly focused on static response which does not consider System dynamics and many changes that occur after Contamination starts. To effectively cope with Contamination threats, however, the emergency response needs to be adaptive in that it should account for the changing circumstances of Contamination and previous actions taken by managers and consumers as well as the stream of new threat information which become available as emergency proceeds. This article describes an ongoing study for development of simulation-optimization models for adaptive emergency response to a Contamination event. Dynamic optimization and event-driven programming is applied to create decision aid tools that support emergency managers in making timely and effective decisions.

Emily Zechman Berglund - One of the best experts on this subject based on the ideXlab platform.

Nathan Sankary - One of the best experts on this subject based on the ideXlab platform.

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