{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T06:30:24Z","timestamp":1773901824351,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T00:00:00Z","timestamp":1678752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["DSAIPA\/DS\/0089\/2018"],"award-info":[{"award-number":["DSAIPA\/DS\/0089\/2018"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/jimaging9030068","type":"journal-article","created":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T06:14:58Z","timestamp":1678774498000},"page":"68","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Hyperparameter Optimization of a Convolutional Neural Network Model for Pipe Burst Location in Water Distribution Networks"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9030-5956","authenticated-orcid":false,"given":"Andr\u00e9","family":"Antunes","sequence":"first","affiliation":[{"name":"Sustain.RD, Escola Superior de Tecnologia de Set\u00fabal, Instituto Polit\u00e9cnico de Set\u00fabal, 2914-508 Set\u00fabal, Portugal"},{"name":"NOVA LINCS, Department of Computer Science, Faculdade de Ci\u00eancias e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2863-7949","authenticated-orcid":false,"given":"Bruno","family":"Ferreira","sequence":"additional","affiliation":[{"name":"INCITE, Escola Superior de Tecnologia do Barreiro, Instituto Polit\u00e9cnico de Set\u00fabal, 2839-001 Lavradio, Portugal"}]},{"given":"Nuno","family":"Marques","sequence":"additional","affiliation":[{"name":"NOVA LINCS, Department of Computer Science, Faculdade de Ci\u00eancias e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2474-7665","authenticated-orcid":false,"given":"Nelson","family":"Carri\u00e7o","sequence":"additional","affiliation":[{"name":"INCITE, Escola Superior de Tecnologia do Barreiro, Instituto Polit\u00e9cnico de Set\u00fabal, 2839-001 Lavradio, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.proeng.2015.08.904","article-title":"Contaminant Intrusion through Leaks in Water Distribution System: Experimental Analysis","volume":"119","author":"Fontanazza","year":"2015","journal-title":"Procedia Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"wpt2008061","DOI":"10.2166\/wpt.2008.061","article-title":"Water Losses\u2019 Assessment in an Urban Water Network","volume":"3","author":"Covas","year":"2008","journal-title":"Water Pract. 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