Data Mining to Identify Contaminant Event Locations in Water Distribution Systems

Huang‬‬‬‬, Jinhui Jeanne; McBean, Edward A.

doi:10.1061/(asce)0733-9496(2009)135:6(466)

Cited by 43 publications

(21 citation statements)

References 9 publications

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“…Huang et al [5] propose a data mining based CSI solution. Perelman et al [15] divide water distribution network into clusters according to the flow and connectivity, and calculate the probabilities of contaminant sources by Bayesian algorithm.…”

Section: Csi Problemmentioning

confidence: 99%

“…Much pioneering work has been devoted to addressing the CSI problem by various methods such as particle backtracking [2] [3], machine learning [4], data mining [5] and so on. Among them, simulation-optimization [6] that couples simulation (e.g., by EPANET 1 ) with heuristic optimization algorithm (e.g., evolutionary algorithms) has been regarded as an efficient and practical way to address the CSI problem for its accuracy and robustness [7].…”

Section: Introductionmentioning

confidence: 99%

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A MapReduce based Parallel Niche Genetic Algorithm for contaminant source identification in water distribution network

Jing

Yan

et al. 2015

Ad Hoc Networks

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Section: Csi Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A MapReduce based Parallel Niche Genetic Algorithm for contaminant source identification in water distribution network

Jing

Yan

et al. 2015

Ad Hoc Networks

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“…Huang and McBean (2009) used a data mining approach with maximum likelihood for an EDS; a solution was determined quickly (within 3 minutes for a 285 node water system with five sensors) using this approach. Shen and McBean (2012) explored ways to reduce false-negatives and falsepositives in event detection using a data mining approach with parallel computing to simulate scenarios.…”

Section: Data Miningmentioning

confidence: 99%

Detection

Jain¹

2014

Drinking Water Security for Engineers, Planners, and Managers

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“…Another procedure is data mining (e.g., Huang and McBean 2009;Shen et al 2009aShen et al , 2009b, which involves 'mining the database' with structural query language (SQL). The data mining procedure consists of three steps.…”

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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Data Mining to Identify Contaminant Event Locations in Water Distribution Systems

Cited by 43 publications

References 9 publications

A MapReduce based Parallel Niche Genetic Algorithm for contaminant source identification in water distribution network

A MapReduce based Parallel Niche Genetic Algorithm for contaminant source identification in water distribution network

Detection

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

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