Trends and applications of machine learning in water supply networks management

Purpose: This study describes the trends and applications of machine learning systems in the management of water supply networks. Machine learning is a field in constant development, and it has a great potential and capability to attain improvements in real industries. The recent tendency of data st...

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Detalhes bibliográficos
Autores: Robles Velasco, Alicia, Muñuzuri, Jesús, Onieva, Luis, Rodríguez Palero, María
Formato: artículo
Fecha de publicación:2021
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:2117/339611
Acesso em linha:https://hdl.handle.net/2117/339611
Access Level:acceso abierto
Palavra-chave:Machine learning
Water-pipes
Predictive control
Supervised learning
Water supply networks
Pipe failures
Predictive systems
Aigua -- Abastament -- Automatització
Aprenentatge automàtic
Aigua -- Canonades
Control predictiu
Àrees temàtiques de la UPC::Economia i organització d'empreses
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
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repository_id_str
spelling Trends and applications of machine learning in water supply networks managementRobles Velasco, AliciaMuñuzuri, JesúsOnieva, LuisRodríguez Palero, MaríaMachine learningWater-pipesPredictive controlMachine learningSupervised learningWater supply networksPipe failuresPredictive systemsAigua -- Abastament -- AutomatitzacióAprenentatge automàticAigua -- CanonadesControl predictiuÀrees temàtiques de la UPC::Economia i organització d'empresesÀrees temàtiques de la UPC::Informàtica::Automàtica i controlPurpose: This study describes the trends and applications of machine learning systems in the management of water supply networks. Machine learning is a field in constant development, and it has a great potential and capability to attain improvements in real industries. The recent tendency of data storage by companies that manage the water supply networks have created a range of possibilities to apply machine learning. One particular case is the prediction of pipe failures based on historical data, which can help to optimally plan the renovation and maintenance tasks. The objective of this work is to define the stages and main characteristics of machine learning systems, focusing on supervised learning methods. Additionally, singularities that are usually found in data from water supply networks are highlighted. Design/methodology/approach: For this purpose, thirteen studies which contain real cases from around the world are discussed. From the data processing to the model validation, a tour of the methods used in each study is carried out. Moreover, the trendiest models are briefly defined together with the mechanisms that best suit their performance. Findings: As a result of the study, it was found that the imbalanced class problem is typical of data from water supply networks where only a small percentage of pipes fail. Consequently, it is recommended to use sampling methods to train classifiers, however, it is not necessary if we are training a regression system. Additionally, scaling and transformation of variables has generally a positive impact on the model’s performance. Currently, cross-validation is almost a requirement to obtain reliable and representative results. This technique is employed in most revised studies to train and validate their models. Originality/value: The use of machine learning systems to predict pipe failures in water supply networks is still a developing field. This study tries to define the advantages and disadvantages of different methods to process data from water supply networks, as well as to train and validate the modelsPeer ReviewedOmniaScience20212021-01-0120212021-02-15journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/339611reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2https://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3396112026-05-27T15:37:01Z
dc.title.none.fl_str_mv Trends and applications of machine learning in water supply networks management
title Trends and applications of machine learning in water supply networks management
spellingShingle Trends and applications of machine learning in water supply networks management
Robles Velasco, Alicia
Machine learning
Water-pipes
Predictive control
Machine learning
Supervised learning
Water supply networks
Pipe failures
Predictive systems
Aigua -- Abastament -- Automatització
Aprenentatge automàtic
Aigua -- Canonades
Control predictiu
Àrees temàtiques de la UPC::Economia i organització d'empreses
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
title_short Trends and applications of machine learning in water supply networks management
title_full Trends and applications of machine learning in water supply networks management
title_fullStr Trends and applications of machine learning in water supply networks management
title_full_unstemmed Trends and applications of machine learning in water supply networks management
title_sort Trends and applications of machine learning in water supply networks management
dc.creator.none.fl_str_mv Robles Velasco, Alicia
Muñuzuri, Jesús
Onieva, Luis
Rodríguez Palero, María
author Robles Velasco, Alicia
author_facet Robles Velasco, Alicia
Muñuzuri, Jesús
Onieva, Luis
Rodríguez Palero, María
author_role author
author2 Muñuzuri, Jesús
Onieva, Luis
Rodríguez Palero, María
author2_role author
author
author
dc.subject.none.fl_str_mv Machine learning
Water-pipes
Predictive control
Machine learning
Supervised learning
Water supply networks
Pipe failures
Predictive systems
Aigua -- Abastament -- Automatització
Aprenentatge automàtic
Aigua -- Canonades
Control predictiu
Àrees temàtiques de la UPC::Economia i organització d'empreses
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
topic Machine learning
Water-pipes
Predictive control
Machine learning
Supervised learning
Water supply networks
Pipe failures
Predictive systems
Aigua -- Abastament -- Automatització
Aprenentatge automàtic
Aigua -- Canonades
Control predictiu
Àrees temàtiques de la UPC::Economia i organització d'empreses
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
description Purpose: This study describes the trends and applications of machine learning systems in the management of water supply networks. Machine learning is a field in constant development, and it has a great potential and capability to attain improvements in real industries. The recent tendency of data storage by companies that manage the water supply networks have created a range of possibilities to apply machine learning. One particular case is the prediction of pipe failures based on historical data, which can help to optimally plan the renovation and maintenance tasks. The objective of this work is to define the stages and main characteristics of machine learning systems, focusing on supervised learning methods. Additionally, singularities that are usually found in data from water supply networks are highlighted. Design/methodology/approach: For this purpose, thirteen studies which contain real cases from around the world are discussed. From the data processing to the model validation, a tour of the methods used in each study is carried out. Moreover, the trendiest models are briefly defined together with the mechanisms that best suit their performance. Findings: As a result of the study, it was found that the imbalanced class problem is typical of data from water supply networks where only a small percentage of pipes fail. Consequently, it is recommended to use sampling methods to train classifiers, however, it is not necessary if we are training a regression system. Additionally, scaling and transformation of variables has generally a positive impact on the model’s performance. Currently, cross-validation is almost a requirement to obtain reliable and representative results. This technique is employed in most revised studies to train and validate their models. Originality/value: The use of machine learning systems to predict pipe failures in water supply networks is still a developing field. This study tries to define the advantages and disadvantages of different methods to process data from water supply networks, as well as to train and validate the models
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01
2021
2021-02-15
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/339611
url https://hdl.handle.net/2117/339611
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

https://creativecommons.org/licenses/by-nc/4.0/
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

https://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv OmniaScience
publisher.none.fl_str_mv OmniaScience
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_ 1869411359223971840
score 15,301603

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