Identification of Contaminant Sources in Water Distribution Systems Using Simulation–Optimization Method: Case Study

Guan, Jiabao; Aral, Mustafa M.; Maslia, Morris L.; Grayman, Walter M.

doi:10.1061/(asce)0733-9496(2006)132:4(252)

Cited by 127 publications

(37 citation statements)

References 9 publications

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“…The goal of this methodology is to decrease the complexity of the problem, while continuing to identify realistic sources of contamination. Guan et al (2006) describes an algorithm that utilizes simulation-optimization and a reduced gradient method (RGM) to solve the source identification problem. Their use of RGM aims to reduce the computation time of the simulation-optimization process.…”

Section: Optimizationmentioning

confidence: 99%

“…Their use of RGM aims to reduce the computation time of the simulation-optimization process. Guan et al (2006) also examined the effect of error in sensor data on the algorithms ability to correctly identify a source. They found that the algorithm could still correctly identify a source, but it was not able to accurately define the release history.…”

Section: Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Using Niched Co-Evolution Strategies to Address Non-Uniqueness in Characterizing Sources of Contamination in a Water Distribution System

Drake¹

2011

World Environmental and Water Resources Congress 2011

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Section: Optimizationmentioning

confidence: 99%

Section: Optimizationmentioning

confidence: 99%

Using Niched Co-Evolution Strategies to Address Non-Uniqueness in Characterizing Sources of Contamination in a Water Distribution System

Drake¹

2011

World Environmental and Water Resources Congress 2011

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“…van Bloemen Waanders et al 2003;Laird et al 2005Laird et al , 2006; Guan et al 2006;Liu et al 2006;Preis & Ostfeld 2007. These optimization approaches include direct methods and simulation-optimization approaches.…”

Section: Contaminant Source Characterization Is Complicated Notmentioning

confidence: 99%

“…Because of the discreteness, nonlinearity and nonconvexity, as well as the limiting assumptions of existing optimization formulations, indirect methods have recently attracted increasing attention. Taking advantage of a simulation-optimization approach, wherein the water distribution system simulation model EPANET was used as a simulator, Guan et al (2006) demonstrated its applicability to nonlinear contaminant sources and releasehistory identification by incorporating the reduced gradient method. Another simulation-optimization approach, proposed by Liu et al (2006), used a multiple population-based evolutionary algorithm to search for a set of contaminant source characteristics that may result in similar sensor observations.…”

Section: Contaminant Source Characterization Is Complicated Notmentioning

confidence: 99%

Logistic regression analysis to estimate contaminant sources in water distribution systems

Liu

Sankarasubramanian

Ranjithan

2010

Journal of Hydroinformatics

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Accidental or intentional contamination in a water distribution system (WDS) has recently attracted attention due to the potential hazard to public health and the complexity of the contaminant characteristics. The accurate and rapid characterization of contaminant sources is necessary to successfully mitigate the threat in the event of contamination. The uncertainty surrounding the contaminants, sensor measurements and water consumption underscores the importance of a probabilistic description of possible contaminant sources. This paper proposes a rapid estimation methodology based on logistic regression (LR) analysis to estimate the likelihood of any given node as a potential source of contamination. Not only does this algorithm yield location-specific probability information, but it can also serve as a prescreening step for simulation-optimization methods by reducing the decision space and thus alleviating the computational burden. The applications of this approach to two example water networks show that it can efficiently rule out numerous nodes that do not yield contaminant concentrations to match the observations. This elimination process narrows down the search space of the potential contamination locations. The results also indicate that the proposed method efficiently yields a good estimation even when some noise is incorporated into the measurements and demand values at the consumption nodes.Key words 9 9 9 9 contaminant source, logistic regression analysis, probabilistic characterization, water distribution systems

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“…The first type used, for example, by Shang et al (2002), employs a methodology to trace back a contaminant particle in discrete time, given a sensor's first detection time and concentration; however, this procedure cannot determine the contaminant release history (contaminant intrusion time, duration, and mass rate) although it is suitable as a pre-step to reduce the search space for an optimization procedure. A second methodology, a simulation-optimization method based on a reduced gradient method (e.g., Guan et al 2006) or genetic algorithm (GA), involves considerable runtime due to the necessity to simulate large numbers of injection events using EPANET (Rossman 2000). To accelerate the GA optimization procedure, parallel GA has been proposed (e.g., Sreepathi et al 2007), which allows simulation of intrusion events with EPANET in parallel; the parallel GA procedure has the following limitations: 1) to utilize this procedure online, a water utility may need to maintain the parallel computing facilities or hardware routinely, since the time of an intrusion event is never known à priori, and hence the computing units may be required at any time.…”

Section: Introductionmentioning

confidence: 99%

Application of parallel computing in data mining for contaminant source identification in water distribution systems

Shen¹,

McBean

2013

Canadian Water Resources Journal

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Contaminant source identification (CSI) procedures are drawing increasing attention due to the possibility of accidental and/or deliberate contaminant intrusion into water distribution systems. However, uncertainties that exist in the modeling have the potential to dramatically impact the capabilities of CSI procedures. Nodal demand uncertainties, as they influence false negative and false positive rates of contaminant detection, are examined. A procedure to quantify the false negative rate is provided, and the false positive issue is shown to be related to a parameter 'm'. Addressing the false positive and negative issues is demonstrated as feasible due to the use of parallel computing in a super-computer, which reduces the elapsed time for 150 scenario simulations from 37.5 hrs to only 15 min in the case study. By increasing the number of scenarios in the database for CSI through the use of a super-computer, the opportunity exists to decrease the false negative rate and reduce the number of false possible intrusion nodes.Keywords: parallel computing; contaminant source identification; uncertainty; EPANET; false positive; false negative Les procédures de l'identification des sources de contamination (CSI) sont en attirant l'attention croissante en raison de la possibilité de contact accidentel et/ou délibéré d'intrusion des contaminants dans les systèmes de distribution d'eau. Donc, les incertitudes qui existent dans la modélisation ont la potentialité de influer considérablement sur les capacités de la procédure de CSI. L'incertitude de la demande indispensables, car elles influencent les faux négatifs et de faux positifs des taux de détection des contaminants, qui sont examinés ici. Une procédure de quantification du taux des faux négatifs est préparée. Ainsi, la question de faux positifs est démontrée être liée à un paramètre 'm'.Se référant aux questions de faux positifs et négatifs, il est démontré comme étant dû à l'utilisation possible du calcul parallèle à l'aide d'un supercalculateur. Ce qui réduit le temps écoulé pour 150 simulations de scenarios de 37,5 heures à seulement 15 minutes dans cette étude. Il existe la possibilité pour diminuer ce taux de faux négatifs en augmentant le nombre de scénarios dans la base de donnée pour le CSI avec l'utilisation d'un supercalculateur et de réduire le nombre des faux noeuds d'intrusion possible.

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Identification of Contaminant Sources in Water Distribution Systems Using Simulation–Optimization Method: Case Study

Cited by 127 publications

References 9 publications

Using Niched Co-Evolution Strategies to Address Non-Uniqueness in Characterizing Sources of Contamination in a Water Distribution System

Using Niched Co-Evolution Strategies to Address Non-Uniqueness in Characterizing Sources of Contamination in a Water Distribution System

Logistic regression analysis to estimate contaminant sources in water distribution systems

Application of parallel computing in data mining for contaminant source identification in water distribution systems

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