Clustering-learning approach to the localization of leaks in water distribution networks

Leak detection and localization in water distribution networks (WDNs) is of great significance for water utilities. This paper proposes a leak localization method that requires hydraulic measurements and structural information of the network. It is composed by an image encoding procedure and a recur...

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Autores: Romero Ben, Luis|||0000-0002-4790-2031, Blesa Izquierdo, Joaquim|||0000-0002-5626-3753, Puig Cayuela, Vicenç|||0000-0002-6364-6429, Cembrano Gennari, Gabriela|||0000-0003-1436-6022
Formato: artículo
Fecha de publicación:2022
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/366702
Acesso em linha:https://hdl.handle.net/2117/366702
https://dx.doi.org/10.1061/(ASCE)WR.1943-5452.0001527
Access Level:acceso abierto
Palavra-chave:Leak detectors
Water -- Distribution
Water distribution network
Leak localization
Deep learning
Graph-based clustering
Real-world network
Detectors de fuites
Aigua -- Distribució
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
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oai_identifier_str oai:upcommons.upc.edu:2117/366702
network_acronym_str ES
network_name_str España
repository_id_str
spelling Clustering-learning approach to the localization of leaks in water distribution networksRomero Ben, Luis|||0000-0002-4790-2031Blesa Izquierdo, Joaquim|||0000-0002-5626-3753Puig Cayuela, Vicenç|||0000-0002-6364-6429Cembrano Gennari, Gabriela|||0000-0003-1436-6022Leak detectorsWater -- DistributionWater distribution networkLeak localizationDeep learningGraph-based clusteringReal-world networkDetectors de fuitesAigua -- DistribucióÀrees temàtiques de la UPC::Informàtica::Automàtica i controlLeak detection and localization in water distribution networks (WDNs) is of great significance for water utilities. This paper proposes a leak localization method that requires hydraulic measurements and structural information of the network. It is composed by an image encoding procedure and a recursive clustering/learning approach. Image encoding is carried out using Gramian Angular Field (GAF) on pressure measurements to obtain images for the learning phase (for all possible leak scenarios). The recursive clustering/learning approach divides the considered region of the network into two sets of nodes using Graph Agglomerative Clustering (GAC), and trains a deep neural network (DNN) to discern the location of each leak between the two possible clusters, using each one of them as inputs to future iterations of the process. The achieved set of DNNs is hierarchically organized to generate a classification tree. Actual measurements from a leak event occurred in a real network are used to assess the approach, comparing its performance with another state-of-the-art technique, and demonstrating the capability of the method to regulate the area of localization depending on the depth of the route through the tree.The authors want to thank the Spanish national project “DEOCS (DPI2016-76493-C3-3-R)” project (which is finished nowadays) by its continuation: “L-BEST Project (PID2020-115905RB-C21) funded by MCIN/ AEI /10.13039/501100011033” and the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). Joaquim Blesa acknowledges the support from the Serra Húnter programPeer Reviewed20222022-01-0120222022-05-03journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/366702https://dx.doi.org/10.1061/(ASCE)WR.1943-5452.0001527reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3667022026-05-27T15:37:01Z
dc.title.none.fl_str_mv Clustering-learning approach to the localization of leaks in water distribution networks
title Clustering-learning approach to the localization of leaks in water distribution networks
spellingShingle Clustering-learning approach to the localization of leaks in water distribution networks
Romero Ben, Luis|||0000-0002-4790-2031
Leak detectors
Water -- Distribution
Water distribution network
Leak localization
Deep learning
Graph-based clustering
Real-world network
Detectors de fuites
Aigua -- Distribució
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
title_short Clustering-learning approach to the localization of leaks in water distribution networks
title_full Clustering-learning approach to the localization of leaks in water distribution networks
title_fullStr Clustering-learning approach to the localization of leaks in water distribution networks
title_full_unstemmed Clustering-learning approach to the localization of leaks in water distribution networks
title_sort Clustering-learning approach to the localization of leaks in water distribution networks
dc.creator.none.fl_str_mv Romero Ben, Luis|||0000-0002-4790-2031
Blesa Izquierdo, Joaquim|||0000-0002-5626-3753
Puig Cayuela, Vicenç|||0000-0002-6364-6429
Cembrano Gennari, Gabriela|||0000-0003-1436-6022
author Romero Ben, Luis|||0000-0002-4790-2031
author_facet Romero Ben, Luis|||0000-0002-4790-2031
Blesa Izquierdo, Joaquim|||0000-0002-5626-3753
Puig Cayuela, Vicenç|||0000-0002-6364-6429
Cembrano Gennari, Gabriela|||0000-0003-1436-6022
author_role author
author2 Blesa Izquierdo, Joaquim|||0000-0002-5626-3753
Puig Cayuela, Vicenç|||0000-0002-6364-6429
Cembrano Gennari, Gabriela|||0000-0003-1436-6022
author2_role author
author
author
dc.subject.none.fl_str_mv Leak detectors
Water -- Distribution
Water distribution network
Leak localization
Deep learning
Graph-based clustering
Real-world network
Detectors de fuites
Aigua -- Distribució
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
topic Leak detectors
Water -- Distribution
Water distribution network
Leak localization
Deep learning
Graph-based clustering
Real-world network
Detectors de fuites
Aigua -- Distribució
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
description Leak detection and localization in water distribution networks (WDNs) is of great significance for water utilities. This paper proposes a leak localization method that requires hydraulic measurements and structural information of the network. It is composed by an image encoding procedure and a recursive clustering/learning approach. Image encoding is carried out using Gramian Angular Field (GAF) on pressure measurements to obtain images for the learning phase (for all possible leak scenarios). The recursive clustering/learning approach divides the considered region of the network into two sets of nodes using Graph Agglomerative Clustering (GAC), and trains a deep neural network (DNN) to discern the location of each leak between the two possible clusters, using each one of them as inputs to future iterations of the process. The achieved set of DNNs is hierarchically organized to generate a classification tree. Actual measurements from a leak event occurred in a real network are used to assess the approach, comparing its performance with another state-of-the-art technique, and demonstrating the capability of the method to regulate the area of localization depending on the depth of the route through the tree.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-01
2022
2022-05-03
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/366702
https://dx.doi.org/10.1061/(ASCE)WR.1943-5452.0001527
url https://hdl.handle.net/2117/366702
https://dx.doi.org/10.1061/(ASCE)WR.1943-5452.0001527
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869402762458955776
score 15,301603

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