Limitations of demand- and pressure-driven modeling for large deficient networks

Braun, Mathias; Piller, Olivier; Deuerlein, Jochen; Mortazavi, Iraj

doi:10.5194/dwes-10-93-2017

Cited by 13 publications

(9 citation statements)

References 19 publications

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“…Two major types of models are generally used for water demand at pipe nodes, i.e., demand-driven model and pressure-driven model. A comparison of both models is described in [6]. A pressure-driven water demand model is used in this study to consider the effects of losing pressure due to change of water demand or leaks.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Machine learning model and strategy for fast and accurate detection of leaks in water supply network

Fan

Zhang

2021

J Infrastruct Preserv Resil

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The water supply network (WSN) is subjected to leaks that compromise its service to the communities, which, however, is challenging to identify with conventional approaches before the consequences surface. This study developed Machine Learning (ML) models to detect leaks in the WDN. Water pressure data under leaking versus non-leaking conditions were generated with holistic WSN simulation code EPANET considering factors such as the fluctuating user demands, data noise, and the extent of leaks, etc. The results indicate that Artificial Neural Network (ANN), a supervised ML model, can accurately classify leaking versus non-leaking conditions; it, however, requires balanced dataset under both leaking and non-leaking conditions, which is difficult for a real WSN that mostly operate under normal service condition. Autoencoder neural network (AE), an unsupervised ML model, is further developed to detect leak with unbalanced data. The results show AE ML model achieved high accuracy when leaks occur in pipes inside the sensor monitoring area, while the accuracy is compromised otherwise. This observation will provide guidelines to deploy monitoring sensors to cover the desired monitoring area. A novel strategy is proposed based on multiple independent detection attempts to further increase the reliability of leak detection by the AE and is found to significantly reduce the probability of false alarm. The trained AE model and leak detection strategy is further tested on a testbed WSN and achieved promising results. The ML model and leak detection strategy can be readily deployed for in-service WSNs using data obtained with internet-of-things (IoTs) technologies such as smart meters.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Machine learning model and strategy for fast and accurate detection of leaks in water supply network

Fan

Zhang

2021

J Infrastruct Preserv Resil

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“…In this relationship, the pressure-dependent nodal demand (c(h)) falls between zero and the desired service demand (q ser i ) depending on the state of the WSNs. In some cases where field data is not available, h min i under which no water can be supplied by the node may be set as the ground elevation at each node [18]. Furthermore, h ser i below which the nodal demand cannot be completely satisfied may be carefully chosen to vary between 14 m to 15 m or more [30,31].…”

Section: Overview Of Pressure-dependent Demand Functionsmentioning

confidence: 99%

“…Most software packages used for water network simulation such as EPANET assume that the water demand at the nodes is fulfilled consistently regardless of the network nodal pressure. This presumption disentangles the problem, even though it is only legitimate for WSNs under normal operating conditions [17,18] where the pressure can be relied upon to satisfactorily fulfill the nodal demand. Nonetheless, practically speaking, a WSN behaves in such a way that if the pressure at a node falls beneath a base level because of some network events, for example, network failures or valve shut down, the flow from this node will significantly reduce, and in extreme cases, the discharge will become zero, irrespective of the actual demand [17].…”

Section: Introductionmentioning

confidence: 99%

Impact of Pressure-Driven Demand on Background Leakage Estimation in Water Supply Networks

Adedeji

Hamam

Abu‐Mahfouz

2019

Water

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Increasing water demand due to urbanization creates a need to develop schemes for managing water supply networks (WSNs). In recent years, hydraulic modeling of WSNs has been used to assess the state of networks in terms of leakage analysis and pressure control. These models are based on demand-driven modeling (DDM) analysis and pressure-driven modeling (PDM) analysis. The former assumes that the nodal demand is fulfilled consistently regardless of the nodal pressure head. The latter appraises the demand as a function of the available pressure head at the nodes. In a previous paper by Adedeji et al. (2017), an algorithm was presented for background leakage detection and estimation in WSNs. The results demonstrated that the algorithm allows the detection of critical pipes and the indication of the nodes where such critical pipes are located for possible pressure control. However, such an algorithm assumes a demand-driven condition of WSNs. In this paper, a pressure-driven modeling is integrated into the developed algorithm with emphasis on its impact on the background leakage estimate. The results obtained are compared to the demand-driven analysis using two WSNs as case studies. The results presented, which consider pipe and node levels, demonstrate that the reliance of the nodal demand on the available pressure head at the node influences the magnitude of the background leakage flow. It is conceived that this investigation might be crucial for the background leakage estimation while considering WSNs operating under pressure-deficient conditions. In this paper, the solution time for both simulation scenarios is also presented.

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“…Further, a number of modeling assumptions introduce epistemic errors to the demand. This includes the nodal agglomeration of demands that occur distributed along a pipe Walski et al (2003) or due to simplification of the network graph Perelman et al (2008) and unrealistic demands due to model deficiencies Braun et al (2017). Pipe diameter and roughness are influenced by corrosive processes and will change over time Boulos et al (2004).…”

Section: Introductionmentioning

confidence: 99%

A Spectral Approach to Uncertainty Quantification in Water Distribution Networks

Braun

Piller

Iollo

et al. 2020

J. Water Resour. Plann. Manage.

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To date, the hydraulics of water distribution networks are calculated using deterministic models.Regarding the fact that many of the parameters in these models are not known exactly, it is important to evaluate the e ects of their uncertainties on the results through Uncertainty Analysis. For the propagation of uncertain parameters, this article for the first time applies the Polynomial Chaos expansion to a hydraulic model and compares the results to classical approaches like the First Order Second Moment method and Monte Carlo simulations. Results presented in this article show that the accuracy of the Polynomial Chaos expansion is on the same level as the Monte Carlo simulation.Further, it is concluded that due to its computational e ciency the Polynomial Chaos Expansion is superior to the Monte Carlo simulation.

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Limitations of demand- and pressure-driven modeling for large deficient networks

Cited by 13 publications

References 19 publications

Machine learning model and strategy for fast and accurate detection of leaks in water supply network

Machine learning model and strategy for fast and accurate detection of leaks in water supply network

Impact of Pressure-Driven Demand on Background Leakage Estimation in Water Supply Networks

A Spectral Approach to Uncertainty Quantification in Water Distribution Networks

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