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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Digital signal processing Jg. 168; S. 105603
Hauptverfasser:	Damonti, Elvio, Bernasconi, Giancarlo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.01.2026
Schlagworte:	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 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)
ISSN:	1051-2004
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!