Water Quality Event Detection in Drinking Water Network

Zhao, Haifeng; Hou, Dibo; Huang, Pingjie; Zhang, Guangxin

doi:10.1007/s11270-014-2183-7

Cited by 13 publications

(7 citation statements)

References 46 publications

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“…The use of water quality anomalies detection from the UV-Vis spectra contributes to the safety of water quality. Detection using the UV-Vis spectra for water quality monitoring is mostly applied for organic contaminant monitoring, as UV-Vis monitoring has the advantage reported earlier, without the need for sample preparation, being regent-free and having a low operational cost compared to the standard laboratory analysis of organics [21,69,70].…”

Section: Anomaly Detection Of Water Qualitymentioning

confidence: 99%

Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review

Shi

Chow

Fabris

et al. 2022

Sensors

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Water quality monitoring is an essential component of water quality management for water utilities for managing the drinking water supply. Online UV-Vis spectrophotometers are becoming popular choices for online water quality monitoring and process control, as they are reagent free, do not require sample pre-treatments and can provide continuous measurements. The advantages of the online UV-Vis sensors are that they can capture events and allow quicker responses to water quality changes compared to conventional water quality monitoring. This review summarizes the applications of online UV-Vis spectrophotometers for drinking water quality management in the last two decades. Water quality measurements can be performed directly using the built-in generic algorithms of the online UV-Vis instruments, including absorbance at 254 nm (UV254), colour, dissolved organic carbon (DOC), total organic carbon (TOC), turbidity and nitrate. To enhance the usability of this technique by providing a higher level of operations intelligence, the UV-Vis spectra combined with chemometrics approach offers simplicity, flexibility and applicability. The use of anomaly detection and an early warning was also discussed for drinking water quality monitoring at the source or in the distribution system. As most of the online UV-Vis instruments studies in the drinking water field were conducted at the laboratory- and pilot-scale, future work is needed for industrial-scale evaluation with ab appropriate validation methodology. Issues and potential solutions associated with online instruments for water quality monitoring have been provided. Current technique development outcomes indicate that future research and development work is needed for the integration of early warnings and real-time water treatment process control systems using the online UV-Vis spectrophotometers as part of the water quality management system.

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Section: Anomaly Detection Of Water Qualitymentioning

confidence: 99%

Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review

Shi

Chow

Fabris

et al. 2022

Sensors

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“…In particular, the evolution of cyberinfrastructure and of online process control instrumentation has led to the development of advanced process control solutions such as supervisory control and data acquisition (SCADA) systems. , Such advances have enabled water utilities to continuously monitor water quality, identify problems, and effectively oversee maintenance issues both remotely and more locally. These systems entail collection of a large volume of raw data that could be, in conjunction with appropriate data analysis techniques, transformed into valuable information that can be leveraged to make proactive decisions to optimize overall performance . In particular, there is surging interest in using ML techniques to identify unusual patterns in raw EWS data as a means of discovering unexpected activities–this is broadly termed anomaly detection …”

Section: Current Applications Of Machine Learning In Esementioning

confidence: 99%

“…These systems entail collection of a large volume of raw data that could be, in conjunction with appropriate data analysis techniques, transformed into valuable information that can be leveraged to make proactive decisions to optimize overall performance. 107 In particular, there is surging interest in using ML techniques to identify unusual patterns in raw EWS data as a means of discovering unexpected activities−this is broadly termed anomaly detection. 108 A typical anomaly detection task in EWS aims to differentiate between natural, expected variations in water quality and unusual or suspicious variations caused by contamination or failure somewhere in the system.…”

Section: Current Applications Of Machine Learning In Esementioning

confidence: 99%

Data Analytics for Environmental Science and Engineering Research

Gupta

Aga

Pruden

et al. 2021

Environ. Sci. Technol.

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The advent of new data acquisition and handling techniques has opened the door to alternative and more comprehensive approaches to environmental monitoring that will improve our capacity to understand and manage environmental systems. Researchers have recently begun using machine learning (ML) techniques to analyze complex environmental systems and their associated data. Herein, we provide an overview of data analytics frameworks suitable for various Environmental Science and Engineering (ESE) research applications. We present current applications of ML algorithms within the ESE domain using three representative case studies: (1) Metagenomic data analysis for characterizing and tracking antimicrobial resistance in the environment; (2) Nontarget analysis for environmental pollutant profiling; and (3) Detection of anomalies in continuous data generated by engineered water systems. We conclude by proposing a path to advance incorporation of data analytics approaches in ESE research and application.

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“…Extensive work has also been done for the detection of contamination events. Other activities focused on the detection by sensor nodes [16][17][18][19][20][21], while others [22,23] on the incorporation of the customer complaints as an additional monitoring tool. Edthofer et al [17] have suggested that the station management module is combined with a self adapting data validation module that marks suspicious data, corrects minor problems, and provides feedback when data is too unreliable.…”

Section: Related Workmentioning

confidence: 99%

“…Edthofer et al [17] have suggested that the station management module is combined with a self adapting data validation module that marks suspicious data, corrects minor problems, and provides feedback when data is too unreliable. Zhao et al [20] proposed the use of conventional water quality parameters (e.g., free chlorine, total chlorine, chloride, pH, turbidity, conductivity, ammonia-nitrogen, nitrate-nitrogen, total organic carbon (TOC), and oxidation-reduction potential (ORP) as surrogate parameters providing indications of contaminants in the water. They also highlighted the need for a comprehensive study about the correlation of a broad selection of contaminants (especially organic chemicals) and the water quality parameters.…”

Section: Related Workmentioning

confidence: 99%

QoWater

Rapousis

Katsarakis

Papadopouli

2015

Proceedings of the 1st ACM International Workshop on Cyber-Physical Systems for Smart Water Networks

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This paper presents QoWater, a novel user-centric crowdsourcing system that enables mobile users to evaluate the quality of drinking, recreational, and irrigation water. The QoWater follows a client-to-server architecture. Via QoWater clients, users upload their feedback about the water quality, together with their position and a timestamp in the spatio-temporal database of QoWater. At the same time, a wireless sensor network monitors the water distribution network (WDN) and uploads the measurements to the QoWater database. We have developed a proof-of-concept implementation of QoWater and performed a preliminary analysis. The paper also highlights the research directions and challenges for developing a robust and effective mechanism.

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Water Quality Event Detection in Drinking Water Network

Cited by 13 publications

References 46 publications

Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review

Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review

Data Analytics for Environmental Science and Engineering Research

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