Pollution Source Identification of Accidental Contamination in Water Distribution Networks

Cristo, Cristiana Di; Leopardi, Angelo

doi:10.1061/(asce)0733-9496(2008)134:2(197)

Cited by 71 publications

(29 citation statements)

References 14 publications

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“…Still, the Boolean measurement algorithm failed to calculate the injection magnitude because of the disconnected reaction between the network delay and the magnitude of concentrations. An optimisation method developed by Cristo and Leopardi [127] aimed to locate an accidental contamination source in water network by using the water fraction matrix concept. The method starts from the concentration data to select a set of candidate nodes from which the source location was identified to minimise the variation between estimated and measured concentrations.…”

Section: Simulation-optimisation Approachmentioning

confidence: 99%

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Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network

Adedoja

Hamam

Khalaf

et al. 2018

Water

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Protection of the water system is paramount due to the negative consequences of contaminated water on the public health. Water resources are one of the critical infrastructures that must be preserved from deliberate and accidental attacks. Water qualities are examined at the treatment plant. However, its quality can substantially be contaminated during transportation from the plant to the consumers' taps. Contamination in water distribution networks (WDNs) is a danger that can have severe consequences on public health as well as an economic and social instability. Water distribution networks are immensely susceptible to deliberate or accidental attacks due to the complex nature of the system. Hence, contamination source identification (CSI) is a topical issue in water distribution systems that require immediate attention of researchers in order to protect mankind from the adverse effect of consuming contaminated water. Usually, a contaminant event can be detected by the water quality monitoring sensors or the contaminant warning system (CWS) installed on the network. Nevertheless, how to derive the source of the contamination from the collected information is a difficult task that must be tackled in order to evaluate the spread of the contamination and for immediate remedial strategies. In the past two decades, considerable efforts and advancement have been made by researchers applying various techniques in order to locate the source of the contamination in WDNs. Each of the techniques has certain limitations and applicability as reported in the literature. This paper presents a comprehensive review of the existing techniques with emphasis on their importance and technical challenges. Despite a series of investigations in this domain, the field is yet to be unified. Hence, open research areas are still available to explore. Consequently, improvement on the existing techniques is necessary and hereby suggested. More importantly, practical application of these techniques offer a major research gap that must be addressed.

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Section: Simulation-optimisation Approachmentioning

confidence: 99%

“…Candelieri et al [147] employed a Bayesian approach to optimise the operation of pumps in water distribution systems. Little effort has been made to estimate the source probabilities [127]. An efficient experimental design for contamination source identification in ground water was discussed by Zhang et al [148].…”

Section: Probabilistic Approachmentioning

confidence: 99%

Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network

Adedoja

Hamam

Khalaf

et al. 2018

Water

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“…Computational requirements may be reduced by using a pre-screening technique that eliminates infeasible solutions to reduce a priori the decision space in which the heuristic procedure must search. One such pre-screening method is the back-tracking algorithm reported by De Sanctis et al (2006), which aims at identifying all possible locations and times that explain contamination incidents detected by water quality sensors.Another approach, proposed by Di Cristo and Leopardi (2008), makes use of the pollution matrix concept to determine a group of candidate nodes that could explain discrete solute concentration measurements. In Liu et al (2011b), a predictive model based on LR analysis was examined to determine the likelihood that any given node is a contaminant source location, using the observed concentration values at sensors.…”

Section: Es-based Adoptmentioning

confidence: 99%

Identifying contaminant sources for water distribution systems using a hybrid method

Liu

Zechman

Mahinthakumar

et al. 2012

Civil Engineering and Environmental Systems

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The rapid discovery of the contaminant source in a water distribution system (WDS) is vital for generating an efficient control strategy during a contamination event. An inverse problem can be constructed, given sensor measurements in a WDS, to identify the contaminant source characteristics by integrating a WDS simulation model with an optimisation method. However, this approach requires numerous computeintensive simulation runs to evaluate potential solutions. This paper reports the findings of an investigation by introducing a hybrid method for the real-time characterisation of a contaminant source. This new method integrates a simulation-optimisation approach with a logistic regression and a local improvement method to expedite the convergence and possibly solve the problem quickly. The results of numerical experiments on two example WDS networks demonstrate the efficiency of the proposed hybrid method for contaminant source characterisation. Effects of various hybrid strategies on the algorithm performance are discussed.

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“…One such prescreening method is the back-tracking algorithm reported by De Sanctis et al (2006), which is able to identify all possible locations and times that explain contamination incidents detected by water quality sensors. Another approach, proposed by Di Cristo & Leopardi (2008), makes use of the pollution matrix concept to determine a group of candidate nodes that could explain discrete solute concentration measurements. The focus of the study presented in this paper is to complement the available search methods by developing and testing a procedure for prescreening the network to assign a relative probability of each node being a candidate potential source.…”

Section: Contaminant Source Characterization Is Complicated Notmentioning

confidence: 99%

Logistic regression analysis to estimate contaminant sources in water distribution systems

Liu

Sankarasubramanian

Ranjithan

2010

Journal of Hydroinformatics

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Accidental or intentional contamination in a water distribution system (WDS) has recently attracted attention due to the potential hazard to public health and the complexity of the contaminant characteristics. The accurate and rapid characterization of contaminant sources is necessary to successfully mitigate the threat in the event of contamination. The uncertainty surrounding the contaminants, sensor measurements and water consumption underscores the importance of a probabilistic description of possible contaminant sources. This paper proposes a rapid estimation methodology based on logistic regression (LR) analysis to estimate the likelihood of any given node as a potential source of contamination. Not only does this algorithm yield location-specific probability information, but it can also serve as a prescreening step for simulation-optimization methods by reducing the decision space and thus alleviating the computational burden. The applications of this approach to two example water networks show that it can efficiently rule out numerous nodes that do not yield contaminant concentrations to match the observations. This elimination process narrows down the search space of the potential contamination locations. The results also indicate that the proposed method efficiently yields a good estimation even when some noise is incorporated into the measurements and demand values at the consumption nodes.Key words 9 9 9 9 contaminant source, logistic regression analysis, probabilistic characterization, water distribution systems

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Pollution Source Identification of Accidental Contamination in Water Distribution Networks

Cited by 71 publications

References 14 publications

Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network

Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network

Identifying contaminant sources for water distribution systems using a hybrid method

Logistic regression analysis to estimate contaminant sources in water distribution systems

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