Moving forward in water distribution network leak identification through an innovative features engineering step
In Italy an average of 40% of the water in transit in Water Distribution Networks (WDNs) is lost, which represents a significant economic loss, and, because of the corresponding energy waste, also a considerable environmental damage. Over the years various methods have been developed for detecting a...
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| Veröffentlicht in: | Digital signal processing Jg. 168; S. 105603 |
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| Abstract | In Italy an average of 40% of the water in transit in Water Distribution Networks (WDNs) is lost, which represents a significant economic loss, and, because of the corresponding energy waste, also a considerable environmental damage. Over the years various methods have been developed for detecting and locating leaks in WDNs, and, in particular, several algorithms have been implemented that use Convolutional Neural Networks (CNNs). They are all based on a training phase run on a representative subset of the WDN data, and the main differences between the various implementations are in the data pre-processing and in the CNN configuration. This paper proposes a new fully Data-Driven approach, where a preliminar features engineering step, performed by a visual analysis of specific data patterns, both in the time domain and in the Fourier domain, allowed us to conceive and identify two paramount features engineering steps: a new effective data pre-processing algorythm and a new configuration of CNN that uses an Overcomplete Autoencoder (Overcomplete AE) topology with residual blocks. These two steps, described in detail in this paper, allowed us to better highlight and identify the anomalies caused by leaks in WDN pressures time series and they permitted, in association with a new original automatic analysis of the reconstruction error made by the Autoencoder, to achieve results that are on the top of the current state of art. Specifically, the whole innovative method is presented in detail exploiting publicly available data, so to be easily reproducible, and, more specifically, for this purpose, the benchmark was run on a synthetic 'LeakDB' dataset of 500 scenarios and the outcomes were then validated through different data obtained from a second and more complex synthetic 'LeakDB' dataset containing 1000 scenarios. Both these datasets are related to a District Metering Area (DMA) of the Hanoi WDN and both are publicly available. |
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| AbstractList | In Italy an average of 40% of the water in transit in Water Distribution Networks (WDNs) is lost, which represents a significant economic loss, and, because of the corresponding energy waste, also a considerable environmental damage. Over the years various methods have been developed for detecting and locating leaks in WDNs, and, in particular, several algorithms have been implemented that use Convolutional Neural Networks (CNNs). They are all based on a training phase run on a representative subset of the WDN data, and the main differences between the various implementations are in the data pre-processing and in the CNN configuration. This paper proposes a new fully Data-Driven approach, where a preliminar features engineering step, performed by a visual analysis of specific data patterns, both in the time domain and in the Fourier domain, allowed us to conceive and identify two paramount features engineering steps: a new effective data pre-processing algorythm and a new configuration of CNN that uses an Overcomplete Autoencoder (Overcomplete AE) topology with residual blocks. These two steps, described in detail in this paper, allowed us to better highlight and identify the anomalies caused by leaks in WDN pressures time series and they permitted, in association with a new original automatic analysis of the reconstruction error made by the Autoencoder, to achieve results that are on the top of the current state of art. Specifically, the whole innovative method is presented in detail exploiting publicly available data, so to be easily reproducible, and, more specifically, for this purpose, the benchmark was run on a synthetic 'LeakDB' dataset of 500 scenarios and the outcomes were then validated through different data obtained from a second and more complex synthetic 'LeakDB' dataset containing 1000 scenarios. Both these datasets are related to a District Metering Area (DMA) of the Hanoi WDN and both are publicly available. |
| ArticleNumber | 105603 |
| Author | Damonti, Elvio Bernasconi, Giancarlo |
| Author_xml | – sequence: 1 givenname: Elvio orcidid: 0009-0009-5657-6512 surname: Damonti fullname: Damonti, Elvio email: elvio.damonti@polimi.it – sequence: 2 givenname: Giancarlo orcidid: 0000-0001-9964-4006 surname: Bernasconi fullname: Bernasconi, Giancarlo email: giancarlo.bernasconi@polimi.it |
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| Cites_doi | 10.1109/ACCESS.2018.2885444 10.1007/BF01386390 10.1080/1573062X.2017.1279191 10.2166/hydro.2015.023 10.1080/08839514.2012.670974 10.1061/(ASCE)WR.1943-5452.0000339 10.1109/TGRS.2020.2984951 10.2352/ISSN.2470-1173.2018.07.MWSF-214 10.1080/1573062X.2014.988733 10.3390/smartcities4040069 10.1016/j.jprocont.2020.08.003 10.1061/(ASCE)WR.1943-5452.0000030 10.2166/aqua.2018.176 10.1007/s00521-021-06666-4 10.21105/joss.05947 |
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| Keywords | Water pressure data features engineering Convolutional neural network (CNN) Overcomplete Autoencoder application Machine learning methods for water leak detection and localization CNN Overcomplete Autoencoder inference errors analysis Leak identification in Water Distribution Networks (WDN) |
| Language | English |
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Manag. doi: 10.1061/(ASCE)WR.1943-5452.0000030 – start-page: 404 year: 2018 ident: 10.1016/j.dsp.2025.105603_bib0002 article-title: Leakage detection and calibration of pipe networks by the inverse transient analysis modified by gaussian functions for leakage simulation publication-title: J. Water Supply: Res. Technol.-Aqua doi: 10.2166/aqua.2018.176 – volume: 34 start-page: 4759 year: 2022 ident: 10.1016/j.dsp.2025.105603_bib0012 article-title: Leak detection and localization in water distribution networks by combining expert knowledge and data-driven models publication-title: Neural Comput. Appl. doi: 10.1007/s00521-021-06666-4 – year: 2023 ident: 10.1016/j.dsp.2025.105603_bib0030 article-title: EPyT: an EPANET-python toolkit for smart water network simulations publication-title: J. Open. Source Softw. doi: 10.21105/joss.05947 |
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| SubjectTerms | CNN Overcomplete Autoencoder inference errors analysis Convolutional neural network (CNN) Overcomplete Autoencoder application Leak identification in Water Distribution Networks (WDN) Machine learning methods for water leak detection and localization Water pressure data features engineering |
| Title | Moving forward in water distribution network leak identification through an innovative features engineering step |
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