Data-driven leak localization in water distribution networks via dictionary learning and graph-based interpolation
Visualitza/Obre
10.1109/CCTA49430.2022.9966160
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/383646
Tipus de documentText en actes de congrés
Data publicació2022
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
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continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional
Abstract
In this paper, we propose a data-driven leak localization method for water distribution networks (WDNs) which combines two complementary approaches: graph-based interpolation and dictionary classification. The former estimates the complete WDN hydraulic state (i.e., hydraulic heads) from real measurements at certain nodes and the network graph. Then, we append to the actual measurements a subset of relevant estimated states to feed and train the dictionary learning scheme. Thus, the meshing of these two methods is explored, and several promising performance results are attained, even deriving different mechanisms to increase the resilience to classical issues (e.g., dimensionality, interpolation errors, etc.). The approach is validated using the L-TOWN benchmark proposed in the BattLeDIM2020 competition.
Descripció
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CitacióIrofti, P. [et al.]. Data-driven leak localization in water distribution networks via dictionary learning and graph-based interpolation. A: IEEE Conference on Control Technology and Applications. "CCTA 2022 : IEEE Conference on Control Technology and Applications, Trieste, Italy, 23-25 august 2022, proceedings book". Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 1265-1270. ISBN 978-1-6654-7339-2. DOI 10.1109/CCTA49430.2022.9966160.
ISBN978-1-6654-7339-2
Versió de l'editorhttps://ieeexplore.ieee.org/document/9966160/
Col·leccions
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Data-driven_Lea ... ph-based_Interpolation.pdf | Postprint | 572,0Kb | Visualitza/Obre |