Real-World Case Studies for Sensor Network Design of Drinking Water Contamination Warning Systems

Murray, Regan; Haxton, Terranna; Hart, William E.; Phillips, Cynthia A.

doi:10.1007/978-1-4614-0189-6_17

Cited by 17 publications

(14 citation statements)

References 12 publications

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“…DSS is used here to take decisions on critical environmental issues. Similar case studies for monitoring of water bodies have been discussed in Murray et al [87]. Some of the researchers around the world are developing different prototypes.…”

Section: Water Body Monitoringmentioning

confidence: 81%

Smart Water Technology for Efficient Water Resource Management: A Review

et al. 2020

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According to the United Nation’s World Water Development Report, by 2050 more than 50% of the world’s population will be under high water scarcity. To avoid water stress, water resources are needed to be managed more securely. Smart water technology (SWT) has evolved for proper management and saving of water resources. Smart water system (SWS) uses sensor, information, and communication technology (ICT) to provide real-time monitoring of data such as pressure, water ow, water quality, moisture, etc. with the capability to detect any abnormalities such as non-revenue water (NRW) losses, water contamination in the water distribution system (WDS). It makes water and energy utilization more efficient in the water treatment plant and agriculture. In addition, the standardization of data format i.e., use of Water Mark UP language 2.0 has made data exchange easier for between different water authorities. This review research exhibits the current state-of-the-art of the on-going SWT along with present challenges and future scope on the mentioned technologies. A conclusion is drawn that smart technologies can lead to better water resource management, which can lead to the reduction of water scarcity worldwide. High implementation cost may act as a barrier to the implementation of SWT in developing countries, whereas data security and its reliability along with system ability to give accurate results are some of the key challenges in its field implementation.

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Section: Water Body Monitoringmentioning

confidence: 81%

Smart Water Technology for Efficient Water Resource Management: A Review

et al. 2020

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“…The estimation error of event detection was computed with respect to the measurements from a moving window [ 24 ]. Moreover, the US Environmental Protection Agency provides the event detection tool-CANARY [ 25 , 26 ] for time series analysis of multiple water quality parameters. Fluctuations in water distribution networks may cause significant variability, a Bayesian belief network was presented to infer the probability of contamination [ 27 ].…”

Section: Related Workmentioning

confidence: 99%

Contamination Event Detection with Multivariate Time-Series Data in Agricultural Water Monitoring

Mao

Ping

et al. 2017

Sensors

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Time series data of multiple water quality parameters are obtained from the water sensor networks deployed in the agricultural water supply network. The accurate and efficient detection and warning of contamination events to prevent pollution from spreading is one of the most important issues when pollution occurs. In order to comprehensively reduce the event detection deviation, a spatial–temporal-based event detection approach with multivariate time-series data for water quality monitoring (M-STED) was proposed. The M-STED approach includes three parts. The first part is that M-STED adopts a Rule K algorithm to select backbone nodes as the nodes in the CDS, and forward the sensed data of multiple water parameters. The second part is to determine the state of each backbone node with back propagation neural network models and the sequential Bayesian analysis in the current timestamp. The third part is to establish a spatial model with Bayesian networks to estimate the state of the backbones in the next timestamp and trace the “outlier” node to its neighborhoods to detect a contamination event. The experimental results indicate that the average detection rate is more than 80% with M-STED and the false detection rate is lower than 9%, respectively. The M-STED approach can improve the rate of detection by about 40% and reduce the false alarm rate by about 45%, compared with the event detection with a single water parameter algorithm, S-STED. Moreover, the proposed M-STED can exhibit better performance in terms of detection delay and scalability.

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“…The main objective of the event detection method is to: (1) identify the possibility of event occurred, (2) characterize the event into subgroup (e.g., spatial area, time duration and severity level), and (3) detect contamination as accurately and early as possible. Achieving optimal time responses are superior for early indications of potential contaminants in WDS [58]. To minimize the spread of outbreaks, rapid and sensitive detection of pathogens is important.…”

Section: Introductionmentioning

confidence: 99%

Detection of contaminants in water supply: A review on state-of-the-art monitoring technologies and their applications

Zulkifli

Rahim

Lau

2018

Sensors and Actuators B: Chemical

217

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a b s t r a c tWater monitoring technologies are widely used for contaminants detection in wide variety of water ecology applications such as water treatment plant and water distribution system. A tremendous amount of research has been conducted over the past decades to develop robust and efficient techniques of contaminants detection with minimum operating cost and energy. Recent developments in spectroscopic techniques and biosensor approach have improved the detection sensitivities, quantitatively and qualitatively. The availability of in-situ measurements and multiple detection analyses has expanded the water monitoring applications in various advanced techniques including successful establishment in hand-held sensing devices which improves portability in real-time basis for the detection of contaminant, such as microorganisms, pesticides, heavy metal ions, inorganic and organic components. This paper intends to review the developments in water quality monitoring technologies for the detection of biological and chemical contaminants in accordance with instrumental limitations. Particularly, this review focuses on the most recently developed techniques for water contaminant detection applications. Several recommendations and prospective views on the developments in water quality assessments will also be included.

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Real-World Case Studies for Sensor Network Design of Drinking Water Contamination Warning Systems

Cited by 17 publications

References 12 publications

Smart Water Technology for Efficient Water Resource Management: A Review

Smart Water Technology for Efficient Water Resource Management: A Review

Contamination Event Detection with Multivariate Time-Series Data in Agricultural Water Monitoring

Detection of contaminants in water supply: A review on state-of-the-art monitoring technologies and their applications

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