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Leak detection and localization in water distribution networks by combining expert knowledge and data-driven models

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Abstract

Leaks represent one of the most relevant faults in water distribution networks (WDN), resulting in severe losses. Despite the growing research interest in critical infrastructure monitoring, most of the solutions present in the literature cannot completely address the specific challenges characterizing WDNs, such as the low spatial resolution of measurements (flow and/or pressure recordings) and the scarcity of annotated data. We present a novel integrated solution that addresses these challenges and successfully detects and localizes leaks in WDNs. In particular, we detect leaks by a sequential monitoring algorithm that analyzes the inlet flow, and then we validate each detection by an ad hoc statistical test. We address leak localization as a classification problem, which we can simplify by a customized clustering scheme that gathers locations of the WDN where, due to the low number of sensors, it is not possible to accurately locate leaks. A relevant advantage of the proposed solution is that it exposes interpretable tuning parameters and can integrate knowledge from domain experts to cope with scarcity of annotated data. Experiments, performed on a real dataset of the Barcelona WDN with both real and simulated leaks, show that the proposed solution can improve the leak detection and localization performance with respect to methods proposed in the literature.

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Availability of data and materials

Leak detection time series in Barcelona DMAs can be found in https://boracchi.faculty.polimi.it/Projects/SelfSimilarityCDT.html while we do not share Limassol and Nova Icària data that are under non-disclosure agreement.

Notes

  1. Sensor placement is a very important aspect which can heavily influence the localization performance [12], but it is not covered in this work where we assume it has already been done.

  2. On DMAs provided with multiple inlets, \(F(\cdot )\) sums the flow measured in all these

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Funding

This work has been funded by the Spanish Ministry of Economy and Competitiveness (MEINCOP), the Spanish State Research Agency (AEI) and by European Regional Development Fund (ERDF) through project DEOCS (ref. DPI2016-76493-C3-3-R) and through grant IJCI-2014-2081, by the European Commission through contract EFFINET (ref. FP7-ICT2011-8-318556), and by the Catalan Agency for Management of University and Research Grants (AGAUR), the European Social Fund (ESF) and the Secretary of University and Research of the Department of Companies and Knowledge of the Government of Catalonia through the grant FI-DGR 2015 (ref. 2015 FI_B 00591).

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Authors and Affiliations

  1. FACTIC, Inc., Urbanització Soldevila, 20, 08650, Cabrianes, Spain

    Adrià Soldevila

  2. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy

    Giacomo Boracchi & Manuel Roveri

  3. Research Center for Supervision, Safety and Automatic Control (CS2AC), Terrassa, Spain

    Sebastian Tornil-Sin & Vicenç Puig

  4. Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Barcelona, Spain

    Sebastian Tornil-Sin & Vicenç Puig

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  1. Adrià Soldevila
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  2. Giacomo Boracchi
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Correspondence to Adrià Soldevila.

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Soldevila, A., Boracchi, G., Roveri, M. et al. Leak detection and localization in water distribution networks by combining expert knowledge and data-driven models. Neural Comput & Applic 34, 4759–4779 (2022). https://doi.org/10.1007/s00521-021-06666-4

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