Detecting Changes in Water Quality Data

McKenna, Sean Andrew; Wilson, M. P.; Klise, Katherine A.

doi:10.1002/j.1551-8833.2008.tb08131.x

Cited by 69 publications

(40 citation statements)

References 26 publications

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“…As summarized by McKenna et al [10], two types of approaches to developing and testing event detection using water quality signals have been examined. First, laboratory and test-loop evaluation of sensors and associated event detection algorithms provides direct measurement of chemical changes in background water quality caused by specific contaminants [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation for a Contamination Detection Method Using Multiple Water Quality Sensors in an Early Warning System

Han

Liu

Smith

2015

Water

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Abstract:In this approach, a method utilizing data series from multivariate parameters to detect contaminant events is discussed and evaluated. Eight water quality sensors (pH, turbidity, conductivity, temperature, oxidation reduction potential, UV-254, nitrate and phosphate) are used in this study and the most commonly used herbicide, glyphosate, is selected as the test contaminant. Variations of all parameters are recorded in real time at different concentrations. The results from the experiment and analysis show that the proposed method with suitable optimization can detect a glyphosate contamination less than 5 min after the introduction of the contaminant using responses from online water quality sensors. The average true positive rate is 95.5%. The study also discusses the impact of the number of sensors on detection performance. The results show that if the number of sensors is reduced from 8 to 5, the true positive rate performance is still good. This indicates that the method is flexible and can be applied using a smaller number of sensors to reduce monitoring costs.

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Section: Introductionmentioning

confidence: 99%

“…The second approach to event detection is based on signal processing and data driven techniques [10,[15][16][17][18][19][20]. For example, Hart et al [15] reported a linear prediction filter (LPF).…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation for a Contamination Detection Method Using Multiple Water Quality Sensors in an Early Warning System

Han

Liu

Smith

2015

Water

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“…In the CANARY event detection tool [10] provided by the US EPA, detection techniques based on linear filters [20] and multivariate distance metrics [15] have been implemented, as well as sensor fusion techniques to take into account the spatial distribution of water quality sensor measurements [16].…”

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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“…On the one hand, the Time Series Models (TSM) capture the temporal redundancy. In particular, we consider in this work the HoltWinters method (Winters, 1960), the Multivariate Differences algorithm (MV) (Mckenna et al, 2008) and an Artificial Neural Network (ANN) trained with historical data to forecast observations (Palani et al, 2008).…”

Section: Data Pipelinementioning

confidence: 99%

“…The Multivariate Distance algorithm (Mckenna et al, 2008) allows to detect changes in a group of parameters. In this work, the group of parameters are the observed by each multi-parameter device.…”

Section: Time Series Modelsmentioning

confidence: 99%

Data analytics methodology for monitoring quality sensors and events in the Barcelona drinking water network

García

Creus²,

Minoves³

et al. 2016

Journal of Hydroinformatics

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Water quality management is a key area to guarantee drinking water safety to the citizens. This task is based on disinfection techniques, such as chlorination, applied to the drinking water network to prevent the growing of microorganisms present in the water. The continuous monitoring of water quality parameters is fundamental to assess sanitary conditions of the drinking water and to detect unexpected events. The whole process is based on the assumption that the information retrieved from quality sensors is totally reliable. But due to the complexity of the calibration and maintenance of these chemical sensors, several factors affect the accuracy of the raw data collected. Consequently, any decision might be based on a non solid base. Therefore, this work presents a data analytics monitoring methodology based on temporal and spatial models to discover if a sensor is detecting a real change on water quality parameters or actually is providing inconsistent information due to some malfunction. The methodology presented, anticipated in 12.4 days, on average, the detection of a sensor problem before the fault was reported by the water utilities (WU) expert using knowledge accumulated with visual analysis. The proposed methodology has been satisfactorily tested on the Barcelona Drinking Water Network.

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Detecting Changes in Water Quality Data

Cited by 69 publications

References 26 publications

Performance Evaluation for a Contamination Detection Method Using Multiple Water Quality Sensors in an Early Warning System

Performance Evaluation for a Contamination Detection Method Using Multiple Water Quality Sensors in an Early Warning System

Contaminant Detection in Urban Water Distribution Networks Using Chlorine Measurements

Data analytics methodology for monitoring quality sensors and events in the Barcelona drinking water network

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