Optimal Layout of Early Warning Detection Stations for Water Distribution Systems Security

Ostfeld, Avi; Salomons, Elad

doi:10.1061/(asce)0733-9496(2004)130:5(377)

Cited by 297 publications

(94 citation statements)

References 11 publications

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“…In water quality management, optimal sensor placement in WDS has also attracted special attention with the aim of identifying contamination sources (Ostfeld and Salomons 2004;Berry et al 2005;Propato, 2006;Berry et al 2006;Shastri1 and Diwekar 2006). They all typically minimize the risk from contamination using sensors for timely detection.…”

Section: Sampling Designmentioning

confidence: 99%

Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

Behzadian

Kapelan

Savić

et al. 2009

Environmental Modelling & Software

126

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Section: Sampling Designmentioning

confidence: 99%

Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

Behzadian

Kapelan

Savić

et al. 2009

Environmental Modelling & Software

126

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show abstract

“…[6,7] introduced an MIP(mixed integer programming) solution for the objective to minimize the expected fraction of population exposed to a contamination. The objective of [8,9] is to ensure that the expected impact of a contamination event is within a pre-specified level, and [8] introduced a formulation based on set cover and solved the problem using genetic algorithm while [9] use a MIP based solution. In order to achieve the objective defined in [10], [11][12][13][14][15][16][17][18][19][20][21] adopted multi-objective optimization by different methods such as heuristic, predator-prey model or local search method and [22] used the submodular property to achieve an approximation guarantee.…”

Section: Related Workmentioning

confidence: 99%

Incremental Sensor Placement Optimization on Water Network

Huang

et al. 2013

Advanced Information Systems Engineering

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Abstract. Sensor placement on water networks is critical for the detection of accidental or intentional contamination event. With the development and expansion of cities, the public water distribution systems in cities are continuously growing. As a result, the current sensor placement will lose its effectiveness in detecting contamination event. Hence, in many real applications, we need to solve the incremental sensor placement (ISP) problem. We expect to find a sensor placement solution that reuses existing sensor deployments as much as possible to reduce cost, while ensuring the effectiveness of contamination detection. In this paper, we propose scenario-cover model to formalize ISP and prove that ISP is NPhard and propose our greedy approaches with provable quality bound. Extensive experiments show the effectiveness, robustness and efficiency of the proposed solutions.

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“…Allmann and Carlson (2005) analyzed the intentional introduction, spread and detection of several known contaminants in a distribution system. Others including Lee and Deininger (1992), Kessler et al (1998), andOstfeld (2004), have sought to optimize the placement of water monitoring stations in distribution systems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…An attempt was made to maximize the sensor's upstream coverage, and distribute sensors evenly across the target area, however, no rigorous mathematical optimization was performed. Others have conducted research into algorithms to determine the optimal placement of sensors (e.g., Lee and Deininger, 1992;Kessler et al, 1998;Ostfeld and Salomons, 2004), which is a topic beyond the scope of this study. Each sensor was assumed to be capable of identifying contaminants from the region of the distribution system upstream of the sensor's location.…”

Section: Application Of Bbns To a Distribution System Case Studymentioning

confidence: 99%

Bayesian Belief Networks to Integrate Monitoring Evidence of Water Distribution System Contamination

Dawsey

Minsker

VanBlaricum

2006

J. Water Resour. Plann. Manage.

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A Bayesian belief network (BBN) methodology is proposed for combining evidence to better characterize contamination events and reduce false positive sensor detections in drinking water distribution systems. A BBN is developed that integrates sensor data with other validating evidence of contamination scenarios. This network is used to graphically express the causal relationships between events such as operational changes or a true contaminant release and consequent observable evidence in an example distribution system. In the BBN methodology proposed here, multiple computer simulations of contaminant transport are used to estimate the prior probabilities of a positive sensor detection. These simulations are run over multiple combinations of possible source locations and initial mass injections for a conservative solute.This approach provides insight into the effect of uncertainties in source mass and location on the detection probability of the sensors. In addition, the simulations identify the upstream nodes that are more likely to result in positive detections. The BBN incorporates the probabilities that result from these simulations, and the network is updated to reflect three demonstration scenarios -a false positive and two true positive sensor detections.iii To my wife and familyiv ACKNOWLEDGEMENTS

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Optimal Layout of Early Warning Detection Stations for Water Distribution Systems Security

Cited by 297 publications

References 11 publications

Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

Incremental Sensor Placement Optimization on Water Network

Bayesian Belief Networks to Integrate Monitoring Evidence of Water Distribution System Contamination

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