A dynamic thresholds scheme for contaminant event detection in water distribution systems

Arad, Jonathan; Housh, Mashor; Perelman, Lina; Ostfeld, Avi

doi:10.1016/j.watres.2013.01.017

Cited by 96 publications

(44 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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%

“…Allgeier et al [17] and Raciti et al [18] utilized artificial neural networks (ANN) and support vector machines (SVM) to classify water quality data into normal and anomalous classes after supervised learning training. Perelman et al [19] and Arad et al [20] reported a general framework that integrates a data-driven estimation model with sequential probability updating to detect quality faults in water distribution systems using multivariate water quality time series. A common feature of the methods mentioned above is that they are merely relying on data process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Han

Liu

Smith

2015

Water

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…The threshold can be set manually (e.g., Pickard, et al 2011) or inferred in an automatic fashion based on the acquired data and trends observed (Arad et al 2013). This approach, however, is vulnerable to faulty measurements that present a large deviation from the pattern.…”

Section: Event Detection In Streams Of Stationary Wdesn Nodesmentioning

confidence: 99%

Framework for Analyzing Environmental Indicators Measurements Acquired by Wireless Distributed Sensory Network – Air Pollution Showcase

Fishbain

2014

Environmental Indicators

View full text Add to dashboard Cite

Distributed continuous in situ monitoring of the environment is an essential component in assessing environmental indicators as their changes take place on different spatial and temporal scales. Recent sensory and communication technological developments have led to the emergence of Wireless Distributed Environmental Sensing Networks (WDESNs) that consist mainly of MicroSensory-Units (MSUs). The set of skills and expertise that are called for developing these WDESNs are from different fields and research disciplines, where each discipline has its own jargon. To allow cross-disciplinary discussion, a comprehensive, yet simple framework for discussing data acquired by WDESNs is presented. The terminology presented here allows for describing complex multi-modal environmental sensory networks and the integration of the observed environmental indicators' into a holistic understanding of the environment. The usage of the presented framework is demonstrated in describing a recently reported data acquired by air-pollution WDESN.

show abstract

“…Perelman et al (2012) tested their approach on a real data set. Arad et al (2013) followed up on the work of Perelman et al (2012) by adding a dynamic threshold scheme. The decision variables were: positive and negative filters, positive and negative dynamic thresholds and data window size.…”

Section: Optimization Techniquesmentioning

confidence: 99%

Detection

Jain¹

2014

Drinking Water Security for Engineers, Planners, and Managers

View full text Add to dashboard Cite

A dynamic thresholds scheme for contaminant event detection in water distribution systems

Cited by 96 publications

References 13 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

Framework for Analyzing Environmental Indicators Measurements Acquired by Wireless Distributed Sensory Network – Air Pollution Showcase

Detection

Contact Info

Product

Resources

About