Hyperparameter Optimization of a Convolutional Neural Network Model for Pipe Burst Location in Water Distribution Networks

Detalhes bibliográficos
Autor(a) principal: Antunes, André
Data de Publicação: 2023
Outros Autores: Ferreira, Bruno, Marques, Nuno, Carriço, Nélson
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/155308
Resumo: Funding Information: The APC was financed by Instituto Politécnico de Setúbal. Publisher Copyright: © 2023 by the authors.
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spelling Hyperparameter Optimization of a Convolutional Neural Network Model for Pipe Burst Location in Water Distribution Networksconvolutional neural networksdeep learninghydraulic modelhyper parameterizationpipe burst locationRadiology Nuclear Medicine and imagingComputer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignElectrical and Electronic EngineeringFunding Information: The APC was financed by Instituto Politécnico de Setúbal. Publisher Copyright: © 2023 by the authors.The current paper presents a hyper parameterization optimization process for a convolutional neural network (CNN) applied to pipe burst locations in water distribution networks (WDN). The hyper parameterization process of the CNN includes the early stopping termination criteria, dataset size, dataset normalization, training set batch size, optimizer learning rate regularization, and model structure. The study was applied using a case study of a real WDN. Obtained results indicate that the ideal model parameters consist of a CNN with a convolutional 1D layer (using 32 filters, a kernel size of 3 and strides equal to 1) for a maximum of 5000 epochs using a total of 250 datasets (using data normalization between 0 and 1 and tolerance equal to max noise) and a batch size of 500 samples per epoch step, optimized with Adam using learning rate regularization. This model was evaluated for distinct measurement noise levels and pipe burst locations. Results indicate that the parameterized model can provide a pipe burst search area with more or less dispersion depending on both the proximity of pressure sensors to the burst or the noise measurement level.DI - Departamento de InformáticaNOVALincsRUNAntunes, AndréFerreira, BrunoMarques, NunoCarriço, Nélson2023-07-14T22:21:10Z2023-03-142023-03-14T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article20application/pdfhttp://hdl.handle.net/10362/155308engAntunes, A., Ferreira, B., Marques, N., & Carriço, N. (2023). Hyperparameter Optimization of a Convolutional Neural Network Model for Pipe Burst Location in Water Distribution Networks. Journal of Imaging, 9(3), [68]. https://doi.org/10.3390/jimaging90300682313-433XPURE: 66124589https://doi.org/10.3390/jimaging9030068info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:37:53Zoai:run.unl.pt:10362/155308Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:56:01.577893Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Hyperparameter Optimization of a Convolutional Neural Network Model for Pipe Burst Location in Water Distribution Networks
title Hyperparameter Optimization of a Convolutional Neural Network Model for Pipe Burst Location in Water Distribution Networks
spellingShingle Hyperparameter Optimization of a Convolutional Neural Network Model for Pipe Burst Location in Water Distribution Networks
Antunes, André
convolutional neural networks
deep learning
hydraulic model
hyper parameterization
pipe burst location
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design
Electrical and Electronic Engineering
title_short Hyperparameter Optimization of a Convolutional Neural Network Model for Pipe Burst Location in Water Distribution Networks
title_full Hyperparameter Optimization of a Convolutional Neural Network Model for Pipe Burst Location in Water Distribution Networks
title_fullStr Hyperparameter Optimization of a Convolutional Neural Network Model for Pipe Burst Location in Water Distribution Networks
title_full_unstemmed Hyperparameter Optimization of a Convolutional Neural Network Model for Pipe Burst Location in Water Distribution Networks
title_sort Hyperparameter Optimization of a Convolutional Neural Network Model for Pipe Burst Location in Water Distribution Networks
author Antunes, André
author_facet Antunes, André
Ferreira, Bruno
Marques, Nuno
Carriço, Nélson
author_role author
author2 Ferreira, Bruno
Marques, Nuno
Carriço, Nélson
author2_role author
author
author
dc.contributor.none.fl_str_mv DI - Departamento de Informática
NOVALincs
RUN
dc.contributor.author.fl_str_mv Antunes, André
Ferreira, Bruno
Marques, Nuno
Carriço, Nélson
dc.subject.por.fl_str_mv convolutional neural networks
deep learning
hydraulic model
hyper parameterization
pipe burst location
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design
Electrical and Electronic Engineering
topic convolutional neural networks
deep learning
hydraulic model
hyper parameterization
pipe burst location
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design
Electrical and Electronic Engineering
description Funding Information: The APC was financed by Instituto Politécnico de Setúbal. Publisher Copyright: © 2023 by the authors.
publishDate 2023
dc.date.none.fl_str_mv 2023-07-14T22:21:10Z
2023-03-14
2023-03-14T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/155308
url http://hdl.handle.net/10362/155308
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Antunes, A., Ferreira, B., Marques, N., & Carriço, N. (2023). Hyperparameter Optimization of a Convolutional Neural Network Model for Pipe Burst Location in Water Distribution Networks. Journal of Imaging, 9(3), [68]. https://doi.org/10.3390/jimaging9030068
2313-433X
PURE: 66124589
https://doi.org/10.3390/jimaging9030068
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 20
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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