Cyberinfrastructure for Contamination Source Characterization in Water Distribution Systems

Sreepathi, Sarat; Mahinthakumar, K.; Zechman, Emily M.; Ranjithan, Ranji; Brill, Downey; Ma, Xiaosong; Laszewski, Gregor von

doi:10.1007/978-3-540-72584-8_139

Cited by 11 publications

(13 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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. Another option is cloud computing, should the internet access and elapsed time in queue before running parallel GA be guaranteed; 2) there is no guarantee for the GA to converge to the global optimum, i.e., the true intrusion node may not be identified; and, 3) there may be the need for simulating duplicate intrusion events, resulting in need for extensive computational power.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…As mentioned earlier, CSC [13] in a Water Distribution System (WDS) is to find the contaminant source locations and their temporal mass loading history. The temporal mass history is defined through values such as as the start time of the contaminant release, duration of release, and the contaminant mass loading during this time.…”

Section: Problem Definitionmentioning

confidence: 99%

“…The MGA code is the optimization engine of the simulation -optimization framework. It calls the simulation component: a PEPANET: The PEPANET is the parallel version of EPANET simulator [13]. It receives a number of contamination source parameters from an input file and divides them into multiple file chunks to different EPANET servers to compute.…”

Section: H I L E T H E P R E D E F I N E D T E R M I N a T I O N C R mentioning

confidence: 99%

“…Urban Water Distribution Systems (WDSs) are vulnerable to accidental and intentional contamination incidents that could result in adverse human health and safety impacts [2]. When a contamination event is detected via sensor networks, municipal authorities are faced with obtaining information about the contamination.…”

Section: Introductionmentioning

confidence: 99%

“…We use a simulation framework to identify optimized locations of sensors that lead to fast detection of contamination sources [1,2]. The optimization engine is based on a Genetic Algorithm (GA) that interprets trial solutions as individuals.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Threat Detection in Urban Water Distribution Systems with Simulations Conducted in Grids and Clouds

Laszewski¹,

Wang²,

Wang³

et al.

Civil-Comp Proceedings

View full text Add to dashboard Cite

The computation of Contaminant Source Characterization (CSC) is a critical research issue in Water Distribution System (WDS) management. We use a simulation framework to identify optimized locations of sensors that lead to fast detection of contamination sources [1,2]. The optimization engine is based on a Genetic Algorithm (GA) that interprets trial solutions as individuals. During the optimization process many thousands of these solutions are generated. For a large WDS, the calculation of these solutions are non-trivial and time consuming. Hence, it is a compute intensive application that requires significant compute resources. Furthermore, we strive to generate solutions quickly in order to respond to the urgency of a response. Grids and Clouds [3] can help in several ways. First, they can provide infrastructure that is of sufficient computational power. Second, they allows the introduction of fault tolerant mechanisms, as ample resources could be made available. Third, due to the power of the available systems fast performance can be achieved. However, the approaches to utilize Grids and Cloud requires the availability of "software stacks" that enable the application developer to more easily use the Infrastructure provided by Grids and Clouds. We provide two distinct platforms.The cyberaide platform: To carry out the calculations we require user-level middleware that can be supporting the workflow [4,5] of the application and manages the resource assignment in an efficient and fault tolerant fashion. To do so we have prototyped the cyberaide framework that provides a convenient command line and portal layer of steering applications on Grids. Internally, we utilize a sophisticated workflow engine that provides the ability to access elementary fault tolerant mechanisms for job scheduling. This includes the management of job replicas and the 1 reaction on late return of results.The Hadoop platform: We report the test results of CSC problem solving on a real Grid test bed -the TeraGrid test bed. In addition, we contrast this system architecture with a Hadoop-based implementation that automatically includes fault tolerance. The later activity has been conducted on FutureGrid [6].We find that the cyberaide platform provided better performance and also allows us to more easily introduce custom designed services. Thus, in case the user has access to Grids and is interested in performance, cyberaide is a good choice.We find Hadoop provides an easy to use programming framework, that abstracts the application user from the infrastructure. FutureGrid and TeraGrid were essential resources for this work. TeraGrid allowed us to explore the Grid infrastructure for this problem, while FutureGrid allowed us to consider the Hadoop platform. AbstractWe present a workflow-based algorithm for identifying threads to an urban water management system. Through Grid computing we provide the necessary high-performance computing resources to deliver quickly solutions to the problem. We prototyped a new middleware called cyberaide, that...

show abstract

Utility of Supercomputers in Trace-Back Algorithms for City-Sized Distribution Systems

Shen¹,

McBean²

2011

Handbook of Water and Wastewater Systems Protection

View full text Add to dashboard Cite

Cyberinfrastructure for Contamination Source Characterization in Water Distribution Systems

Cited by 11 publications

References 5 publications

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

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

Threat Detection in Urban Water Distribution Systems with Simulations Conducted in Grids and Clouds

Utility of Supercomputers in Trace-Back Algorithms for City-Sized Distribution Systems

Contact Info

Product

Resources

About