Distribution System Monitoring Research at Charleston Water System

Cook, John; Byrne, J. F.; Daamen, Ruby C.; Roehl, Edwin A.

doi:10.1061/40941(247)99

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…An off-the-shelf solution is provided by Hach HST, in which various water quality parameters are considered to calculate a distance measure through a proprietary algorithm, which is used for contamination detection and isolation [18]. Field trials for contamination detection have been investigated in [4]. Data mining techniques used in computer networks have been applied to detect contaminations in water systems [26], using a set of normal-behaviour clusters.…”

Section: Contamination Detection Methodologiesmentioning

confidence: 99%

Contaminant Detection in Urban Water Distribution Networks Using Chlorine Measurements

Ηλιάδης

Polycarpou

2013

Critical Information Infrastructures Security

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Abstract. In this work we present a contamination detection methodology for water distribution networks. The proposed detection method is based on chlorine sensor measurements, which are compare to certain computed upper and lower periodic bounds. The bounds are computed using randomized simulations aimed at capturing the variations in chlorine concentration due to the significant uncertainty in the water demand patterns, average nodal consumptions, roughness parameters and reaction coefficients. The proposed method is applied to a set of high-impact contamination fault scenarios using a benchmark distribution network, for which on-line chlorine concentration sensors are assumed to have been installed at certain locations following an optimization procedure. The results indicate that by using the periodic bounds computed from the randomized simulations, for the proposed benchmark, contamination events are detected within reasonable time.

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Section: Contamination Detection Methodologiesmentioning

confidence: 99%

Contaminant Detection in Urban Water Distribution Networks Using Chlorine Measurements

Ηλιάδης

Polycarpou

2013

Critical Information Infrastructures Security

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“…The current methods to detect anomalies in the WDN can be categorized in three groups including statistical (Allgeier, Murray, Mckenna, & Shalvi, 2005;McKenna, Klise, & Wilson, 2007;Murray, 2010;Shang, Uber, Murray, & Janke, 2007), empirical AI-based (Allgeier et al, 2005;Raciti, Cucurull, & NadjmTehrani, 2012), and data mining (Koch & McKenna, 2011;McKenna et al, 2007;Murray, 2010;Yang, Goodrich, Clark, & Li, 2008;bib50) methods. Some of these event detection systems are only suited for data collected in a single monitoring station to indicate the occurrence of a contamination event (Byer & Carlson, 2005;Cook, Byrne, Daamen, & Roehl, 2006;Murray, 2010;Yang, Haught, & Goodrich, 2009) while other methods are based on two monitoring locations to improve the aggregation results using one of the nodes as the reference to compensate for the calibration error of the other node, varying time delays, and background noise (Yang et al, 2008;McKenna et al, 2007). Kumar et al (2007) studied some of these event detection allow incorporating various types of uncertainty in causal spatiotemporal relationships of WQPs to water quality events.…”

Section: Introductionmentioning

confidence: 99%

Multi-level information fusion for spatiotemporal monitoring in water distribution networks

Aminravan

Sadiq

Hoorfar

et al. 2015

Expert Systems with Applications

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“…A second approach to event detection is based on signal processing and pattern recognition techniques borrowed from other fields. This approach uses background water quality observations as measured by sensors deployed in distribution systems with numerically simulated events inserted into these observations as test data (Cook et al, 2006; Klise & McKenna, 2006; McKenna et al, 2006). This second testing approach has the advantage of employing actual background variation in water quality; however, it does not necessarily provide accurate water quality sensor response to a particular contaminant.…”

mentioning

confidence: 99%

Detecting Changes in Water Quality Data

McKenna¹,

Wilson²,

Klise³

2008

Journal AWWA

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Timely deployment of contaminant warning systems requires on‐line sensors and advancement of data analysis and decision support systems to accurately detect water quality changes. As a demonstration of event detection in water quality data, three water quality change‐detection algorithms were developed and used to detect changes in water quality observed at four locations within a distribution system. Each data set was “spiked” with simulated anomalous water quality values of 1 h duration and 10 levels of spike strength. The receiver operating characteristic (ROC) curve is proposed as an objective means of assessing and comparing water quality change‐detection algorithm results that is also able to provide the tradeoff between missed detections and false alarms on a single plot. The area under the ROC curve provides a measure of the sensitivity and specificity of each algorithm for each spike strength and location. Resulting areas under the ROC curve range from 0.46 for spike strengths of 1.0 (background) to 0.98 for strengths of 3.5 standard deviations from the mean, where an ROC curve area of 1.0 indicates perfect detection.

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Distribution System Monitoring Research at Charleston Water System

Cited by 6 publications

References 5 publications

Contaminant Detection in Urban Water Distribution Networks Using Chlorine Measurements

Contaminant Detection in Urban Water Distribution Networks Using Chlorine Measurements

Multi-level information fusion for spatiotemporal monitoring in water distribution networks

Detecting Changes in Water Quality Data

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