Algorithm for Early Warning System for Contamination in Water Network

Antonowicz, Ariel; Bałut, Alicja; Urbaniak, Andrzej; Zakrzewski, Przemysław

doi:10.1109/carpathiancc.2019.8765966

Cited by 4 publications

(6 citation statements)

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“…EPANET quickly became an educational and research tool to better understand the movement and fate of drinking water constituents within network systems [16]. Moreover, this tool facilitates: hydraulic and water quality simulation, supplying a mixture of water from various sources to recipients, calculating the age of water throughout a system, tracking the spread of contamination, and specification of loss of chlorine residuals [16,17]. The water network tool for resilience (WNTR) is a Python package designed to simulate and analyze the resilience of the water distribution network [11,16,17,26].…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, this tool facilitates: hydraulic and water quality simulation, supplying a mixture of water from various sources to recipients, calculating the age of water throughout a system, tracking the spread of contamination, and specification of loss of chlorine residuals [16,17]. The water network tool for resilience (WNTR) is a Python package designed to simulate and analyze the resilience of the water distribution network [11,16,17,26]. Beyond the possibilities offered by EPANET, WNTR facilitates the modeling of untypical situations such as power outages, incidental contamination events, and pipe breaks, leaks modeling, earthquakes, etc.…”

Section: Methodsmentioning

confidence: 99%

“…large industrial plants). The structure of the network is a centralized network (all small villages are connected to the nearest biggest network) [17]. Based on the chosen water network models, Python interpreter, and necessary libraries (including the WNTR and Ray), a list of preliminary assumptions was prepared containing information necessary to conduct the simulation.…”

Section: Case Studymentioning

confidence: 99%

“…The results of the presented scenarios show that the proposed algorithms are a good development base for decision support systems or early warning systems. Decision-makers and system developers should consider that the usefulness of this type of solution depends to a considerable extent on their coherence and integration with system handling failures [17]. The integration of the proposed approach with DSS or EWS directly determines the reliability of the results obtained [11,17].…”

Section: Conclusion 51 General Thoughts -The Pros and Consmentioning

confidence: 99%

“…Decision-makers and system developers should consider that the usefulness of this type of solution depends to a considerable extent on their coherence and integration with system handling failures [17]. The integration of the proposed approach with DSS or EWS directly determines the reliability of the results obtained [11,17]. Using the hydraulic model of the water distribution system and initial information on the algorithm return list of valves needed to be closed.…”

Section: Conclusion 51 General Thoughts -The Pros and Consmentioning

confidence: 99%

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Bulletin of the Polish Academy of Sciences: Technical Sciences

Antonowicz

Urbaniak

2022

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The article discusses an example of the use of graph search algorithms with the trace of water analysis and aggregation of failures in the occurrence of a large number of failures in the water supply system (WSS). In the event of a disaster, based on the water distribution system (WDS) network model, information about detected failures, the condition and location of valves, the number of repair teams, criticality analysis, the coefficient of prioritization of individual network elements, and selected objective function, the algorithm proposes the order of repairing the failures. The approach proposed by the authors of the article assumes the selection of the following objective functions: minimizing the time of lack of access to drinking water (with or without prioritization) and minimizing failure repair time (with or without failure aggregation). The algorithm was tested on three different water networks (small, medium, and large numbers of nodes) and three different scenarios (different numbers of failures and valves in the water network) for each selected water network. The results were compared to a valve designation approach for closure using an adjacency matrix and a strategic valve management model (SVMM).

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