Network hydraulics inclusion in water quality event detection using multiple sensor stations data

Oliker, Nurit; Ostfeld, Avi

doi:10.1016/j.watres.2015.04.036

Cited by 21 publications

(14 citation statements)

References 16 publications

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“…The proposed method is demonstrated using two different networks and is compared with the method that uses only temporal analysis. The indexes of the performance evaluation chosen here are True Positive Rate (TPR) and False Positive Rate (FPR), which are widely used in performance examination [16].…”

Section: Methodsmentioning

confidence: 99%

“…The average flow time between upstream and downstream stations is obtained using the hydraulic information from Oliker and Ostfeld [16]. The unit of pattern Figure 3a shows five stations, marked A-E. Due to the conditional independence of the Bayesian Network, the conditional probability only exists between a node and its parent nodes that connected directly to it.…”

Section: Spatial Analysis Using Causal Relationship Based On Multi-stmentioning

confidence: 99%

“…The average flow time between upstream and downstream stations is obtained using the hydraulic information from Oliker and Ostfeld [16]. The unit of pattern of EPANET is one hour; the flow velocity and length of pipe are used to derive the time the water takes to flow from one station to its downstream station for each hour.…”

Section: Spatial Analysis Using Causal Relationship Based On Multi-stmentioning

confidence: 99%

“…The Bayesian Network was used for the spatial detection of a river's network and Markov chain for the temporal detection to improve the performance of the detection. Oliker and Ostfeld [16] improved the performance of event detection by using a model that incorporates hydraulic simulation information into the overall event detection process of spatially distributed sensors, resulting in a decrease of false alarms. Stoianov et al [17] developed a method for the network construction of water quality event detection and also analysed the relationship between different nodes and water quality variables based on the frequency-domain analysis method.…”

Section: Introductionmentioning

confidence: 99%

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Contamination Event Detection Method Using Multi-Stations Temporal-Spatial Information Based on Bayesian Network in Water Distribution Systems

Xie

et al. 2017

Water

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Abstract:As a core part of protecting water quality safety in water distribution systems, contamination event detection requires high accuracy. Previously, temporal analysis-based methods for single sensor stations have shown limited performance as they fail to consider spatial information. Besides, abundant historical data from multiple stations are still underexploited in causal relationship modelling. In this paper, a contamination event detection method is proposed, in which both temporal and spatial information from multi-stations in water distribution systems are used. The causal relationship between upstream and downstream stations is modelled by Bayesian Network, using the historical water quality data and hydraulic data. Then, the spatial abnormal probability for one station is obtained by comparing its current causal relationship with the established model. Meanwhile, temporal abnormal probability is obtained by conventional methods, such as an Autoregressive (AR) or threshold model for the same station. The integrated probability that is calculated employed temporal and spatial probabilities using Logistic Regression to determine the final detection result. The proposed method is tested over two networks and its detection performance is evaluated against results obtained from traditional methods using only temporal analysis. Results indicate that the proposed method shows higher accuracy due to its increased information from both temporal and spatial dimensions.

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Section: Methodsmentioning

confidence: 99%

Section: Spatial Analysis Using Causal Relationship Based On Multi-stmentioning

confidence: 99%

Section: Spatial Analysis Using Causal Relationship Based On Multi-stmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Contamination Event Detection Method Using Multi-Stations Temporal-Spatial Information Based on Bayesian Network in Water Distribution Systems

Xie

et al. 2017

Water

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“…An initial growth in water quality sensor fabrication and optimal sensor placement for event detection has been introduced over the past several years [40][41][42][43]. The synthesis of the aligned water sensors, also known as sensor placement approach, could improve the device detection performance by increasing the detection sensitivity [44,45]. Deploying sensors in water distribution system allow the measurements of temperature, pH, turbidity, conductivity, oxidation reduction potential (ORP), UV-254, nitrate-nitrogen and phosphate both on-line and off-line.…”

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

232

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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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Water Quality Sensor Placement: A Multi-Objective and Multi-Criteria Approach

Brentan

Carpitella

Barros

et al. 2021

Water Resour Manage

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To satisfy their main goal, namely providing quality water to consumers, water distribution networks (WDNs) need to be suitably monitored. Only well designed and reliable monitoring data enables WDN managers to make sound decisions on their systems. In this belief, water utilities worldwide have invested in monitoring and data acquisition systems. However, good monitoring needs optimal sensor placement and presents a multi-objective problem where cost and quality are conflicting objectives (among others

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Network hydraulics inclusion in water quality event detection using multiple sensor stations data

Cited by 21 publications

References 16 publications

Contamination Event Detection Method Using Multi-Stations Temporal-Spatial Information Based on Bayesian Network in Water Distribution Systems

Contamination Event Detection Method Using Multi-Stations Temporal-Spatial Information Based on Bayesian Network in Water Distribution Systems

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

Water Quality Sensor Placement: A Multi-Objective and Multi-Criteria Approach

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