Automated Detection of Pipe Bursts and Other Events in Water Distribution Systems

Romano, Michele; Kapelan, Zoran; Savić, Dragan

doi:10.1061/(asce)wr.1943-5452.0000339

Cited by 168 publications

(92 citation statements)

References 21 publications

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“…Various data-driven methods have been proposed for WDS pipe burst detection: artificial intelligence [19,20], state estimation [16,21,22], the Bayesian approach [23], classification [24,25], and SPC [15][16][17][26][27][28]. Wu and Liu [29] recently reviewed and classified data-driven approaches; please refer to them for more details of each method.…”

Section: Pipe Burst Detection Method: Western Electric Company (Wec) mentioning

confidence: 99%

Robust Meter Network for Water Distribution Pipe Burst Detection

Jung

Kim

2017

Water

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Abstract:A meter network is a set of meters installed throughout a water distribution system to measure system variables, such as the pipe flow rate and pressure. In the current hyper-connected world, meter networks are being exposed to meter failure conditions, such as malfunction of the meter's physical system and communication system failure. Therefore, a meter network's robustness should be secured for reliable provision of informative meter data. This paper introduces a multi-objective optimal meter placement model that maximizes the detection probability, minimizes false alarms, and maximizes the robustness of a meter network given a predefined number of meters. A meter network's robustness is defined as its ability to consistently provide quality data in the event of meter failure. Based on a single-meter failure simulation, a robustness indicator for the meter network is introduced and maximized as the third objective of the proposed model. The proposed model was applied to the Austin network to determine the independent placement of pipe flow and pressure meters with three or five available meters. The results showed that the proposed model is a useful tool for determining meter locations to secure high detectability and robustness.

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Section: Pipe Burst Detection Method: Western Electric Company (Wec) mentioning

confidence: 99%

Robust Meter Network for Water Distribution Pipe Burst Detection

Jung

Kim

2017

Water

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“…Data collected by these devices provides a potentially useful source of information for reproducing and predicting the behaviour of a WDN. This data is in the form of time series (i.e., a data stream consisting of one or more variables whose value is a function of time) and, when used in conjunction with reproductions/predictions of the WDN behaviour, has the potential to enable fast and economic detection and location of pipe bursts (Romano et al 2014a;. In view of this and of the limitations of the aforementioned conventional solutions to the burst detection and location problem, it is clear that new and more efficient techniques are needed for efficiently and effectively exploiting water industry's pressure and flow data.…”

Section: Introductionmentioning

confidence: 99%

“…An example of data-driven technique applied to the leakage detection and location problem are the Artificial neural networks (ANNs) combined with fuzzy logic technology (Mounce et al, 2006; , or as self-organising map (Aksela et al, 2009), or combined with a probabilistic inference engine based on Bayesian networks (Romano et al, 2014a;. Other examples are the application of Kalman filtering techniques (Jarret et al, 2006) (Ye and Fenner, 2011), support vector machines , geostatistical techniques (Kriging) (Romano et al, 2013) and comparison of flow pattern distributions (Van Thienen, 2013).…”

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confidence: 99%

Detecting anomalies in water distribution networks using EPR modelling paradigm

Laucelli

Romano

Savić

et al. 2015

Journal of Hydroinformatics

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Sustainable management of water distribution networks (WDNs) requires effective exploitation of available data from pressure and flow devices. Nowadays, water companies are collecting a large amount of such data and they need to be managed correctly and analysed effectively using appropriate techniques. Furthermore, water companies need to balance the data gathering and handling costs with the benefits of extracting information useful for making reliable operational decisions. Among different approaches developed in the last few decades, those implementing data mining techniques for analysing pressure and flow data appear very promising. This is because they can automate mundane tasks involved in the data analysis process and efficiently deal with the vast amount of, often imperfect, sensor data collected. Furthermore, they rely on empirical observations of a WDN behaviour over time, allowing reproducing/predicting possible future behaviour without employing hydraulic simulation of the network, which require continuous/iterative calibration of the model based on on-line fresh data. This paper investigates the effectiveness of the evolutionary polynomial regression (EPR) paradigm to reproduce and predict the behaviour of a WDN using on-line data recorded by low-cost pressure/flow devices. Using data from a real district metered area (DMA), the case study presented in this paper shows that by using the EPR paradigm a model can be built which enables to accurately reproduce and predict the WDN behaviour over time and detect flow anomalies due to possible unreported bursts or unknown increase of water withdrawal. Such an EPR model might be integrated into an early warning system to raise alarms when anomalies are detected.

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“…In hydraulic models, well-known hydraulic equations are solved to calculate main hydraulic parameters; such as Net head, pipe length, velocity and diameter of pipe, at many points for the described WDN and the obtained results are displayed in tabular and graphical forms to be evaluated by the users [1][2][3][4]. The success of hydraulic model predictions depends on accurate determination/estimation of input parameters and model calibration and verification studies.…”

Section: Introductionmentioning

confidence: 99%

Estimation of hazen williams’s constant for a residential water distribution network; GMDH and PSO approach

Tyagi¹,

Majumder²,

Kant³

et al. 2018

IJET

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Hazen-William equation is used to estimate the Fluid flow in closed channel. There are various models for estimation of pipe flow, however the accuracy and reliability of models varies due to the empirical nature of the Hazen-William constant .the applicability of model also become constrained due to the dependency of constant on pipe material, dimension and flow potential. Different type of pipeline arranged in different Networks will require different value of the constant and is generally retrieved from the data collected for the pipe network. The case dependency of the model has makes the model erroneous and often subjective that is why the present study tries to propose a model which can be used for any network where the output will depend upon the inputs. In this aspect the soft computation techniques: -GMDH and PSO was utilized in an unconventional way to establish the value of CHW =f (H, L, V, D). According to result the GMDH becomes the better model than the PSO where the accuracy is about 76.315%.

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Automated Detection of Pipe Bursts and Other Events in Water Distribution Systems

Cited by 168 publications

References 21 publications

Robust Meter Network for Water Distribution Pipe Burst Detection

Robust Meter Network for Water Distribution Pipe Burst Detection

Detecting anomalies in water distribution networks using EPR modelling paradigm

Estimation of hazen williams’s constant for a residential water distribution network; GMDH and PSO approach

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