Distributed Sensor Fusion in Water Quality Event Detection

Koch, Mark W.; McKenna, Sean Andrew

doi:10.1061/(asce)wr.1943-5452.0000094

Cited by 42 publications

(20 citation statements)

References 19 publications

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“…As utilities continue to add monitoring stations within distribution networks, the concept of ''distributed detection,'' where information from multiple monitoring stations is combined in real time to provide an integrated detection capability, will likely become possible. Koch and McKenna (2011) propose an approach for combining data from multiple locations to reduce false background alarms. Recent development and testing of an approach to distributed detection has shown that integration of EDS results across a network can significantly reduce false positive detections and help to provide better estimates of a contaminant source location (Koch and McKenna 2011).…”

Section: Water Quality Sensors and Contamination Event Detectionmentioning

confidence: 99%

Protecting Water Supply Critical Infrastructure: An Overview

Janke

Tryby

Clark

2013

Securing Water and Wastewater Systems

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Section: Water Quality Sensors and Contamination Event Detectionmentioning

confidence: 99%

Protecting Water Supply Critical Infrastructure: An Overview

Janke

Tryby

Clark

2013

Securing Water and Wastewater Systems

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“…The results of the technique indicated a good correlation and its capability on the numbers of cases examined. The combination of data from several monitoring sensors to minimise false background alarms was proposed by Koch et al [166]. They used the Kulldorffs scan test to statistically locate the important clusters of identification based on the location and time of isolated detections as points leading to a random space-time point process.…”

Section: Other Approachesmentioning

confidence: 99%

Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network

Adedoja

Hamam

Khalaf

et al. 2018

Water

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Protection of the water system is paramount due to the negative consequences of contaminated water on the public health. Water resources are one of the critical infrastructures that must be preserved from deliberate and accidental attacks. Water qualities are examined at the treatment plant. However, its quality can substantially be contaminated during transportation from the plant to the consumers' taps. Contamination in water distribution networks (WDNs) is a danger that can have severe consequences on public health as well as an economic and social instability. Water distribution networks are immensely susceptible to deliberate or accidental attacks due to the complex nature of the system. Hence, contamination source identification (CSI) is a topical issue in water distribution systems that require immediate attention of researchers in order to protect mankind from the adverse effect of consuming contaminated water. Usually, a contaminant event can be detected by the water quality monitoring sensors or the contaminant warning system (CWS) installed on the network. Nevertheless, how to derive the source of the contamination from the collected information is a difficult task that must be tackled in order to evaluate the spread of the contamination and for immediate remedial strategies. In the past two decades, considerable efforts and advancement have been made by researchers applying various techniques in order to locate the source of the contamination in WDNs. Each of the techniques has certain limitations and applicability as reported in the literature. This paper presents a comprehensive review of the existing techniques with emphasis on their importance and technical challenges. Despite a series of investigations in this domain, the field is yet to be unified. Hence, open research areas are still available to explore. Consequently, improvement on the existing techniques is necessary and hereby suggested. More importantly, practical application of these techniques offer a major research gap that must be addressed.

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“…Data-mining methods, such as K-means classification and the multivariate nearest-neighbor (MV-NN) algorithm, which combine different water-quality parameters and location information, are also used for protecting drinking water systems [2,5,6,9,10]. In addition to the above three categories, several researchers introduced data-fusion methods to combine various types of information, for example, operational data [11], additional station-specific features [8] and data from multiple monitoring stations [12] to improve the detection of water-contamination events. Although research on water-contamination event detection has proliferated in recent years, careful analysis of those methods reveals that many of them still have several shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Detection of water-quality contamination events based on multi-sensor fusion using an extented Dempster–Shafer method

Hou

Huang

et al. 2013

Meas. Sci. Technol.

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This study presents a method for detecting contamination events of sources of drinking water based on the Dempster-Shafer (D-S) evidence theory. The detection method has the purpose of protecting water supply systems against accidental and intentional contamination events. This purpose is achieved by first predicting future water-quality parameters using an autoregressive (AR) model. The AR model predicts future water-quality parameters using recent measurements of these parameters made with automated (on-line) water-quality sensors. Next, a probabilistic method assigns probabilities to the time series of residuals formed by comparing predicted water-quality parameters with threshold values. Finally, the D-S fusion method searches for anomalous probabilities of the residuals and uses the result of that search to determine whether the current water quality is normal (that is, free of pollution) or contaminated. The D-S fusion method is extended and improved in this paper by weighted averaging of water-contamination evidence and by the analysis of the persistence of anomalous probabilities of water-quality parameters. The extended D-S fusion method makes determinations that have a high probability of being correct concerning whether or not a source of drinking water has been contaminated. This paper's method for detecting water-contamination events was tested with water-quality time series from automated (on-line) water quality sensors. In addition, a small-scale, experimental, water-pipe network was tested to detect water-contamination events. The two tests demonstrated that the extended D-S fusion method achieves a low false alarm rate and high probabilities of detecting water contamination events.

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Distributed Sensor Fusion in Water Quality Event Detection

Cited by 42 publications

References 19 publications

Protecting Water Supply Critical Infrastructure: An Overview

Protecting Water Supply Critical Infrastructure: An Overview

Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network

Detection of water-quality contamination events based on multi-sensor fusion using an extented Dempster–Shafer method

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