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Noncontact Automatic Water-Level Assessment and Prediction in an Urban Water Stream Channel of a Volcanic Island Using Deep Learning

2024Journal article Open access
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dc.contributor.authorMendonça, Fabio
dc.contributor.authorMostafa, Sheikh Shanawaz
dc.contributor.authorDias, Fernando Morgado
dc.contributor.authorAzevedo, Joaquim Amândio
dc.contributor.authorRavelo-García, Antonio G.
dc.contributor.authorNavarro-Mesa, Juan L.
dc.date.accessioned2024-04-26T08:57:50Z
dc.date.available2024-04-26T08:57:50Z
dc.date.issued2024
dc.description.abstractTraditional methods for water-level measurement usually employ permanent structures, such as a scale built into the water system, which is costly and laborious and can wash away with water. This research proposes a low-cost, automatic water-level estimator that can appraise the level without disturbing water flow or affecting the environment. The estimator was developed for urban areas of a volcanic island water channel, using machine learning to evaluate images captured by a low-cost remote monitoring system. For this purpose, images from over one year were collected. For better performance, captured images were processed by converting them to a proposed color space, named HLE, composed of hue, lightness, and edge. Multiple residual neural network architectures were examined. The best-performing model was ResNeXt, which achieved a mean absolute error of 1.14 cm using squeeze and excitation and data augmentation. An explainability analysis was carried out for transparency and a visual explanation. In addition, models were developed to predict water levels. Three models successfully forecasted the subsequent water levels for 10, 60, and 120 min, with mean absolute errors of 1.76 cm, 2.09 cm, and 2.34 cm, respectively. The models could follow slow and fast transitions, leading to a potential flooding risk-assessment mechanism.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMendonça, F.; Mostafa, S.S.; Morgado-Dias, F.; Azevedo, J.A.; Ravelo-García, A.G.; Navarro-Mesa, J.L. Noncontact Automatic Water Level Assessment and Prediction in an Urban Water Stream Channel of a Volcanic Island Using Deep Learning. Electronics 2024, 13, 1145. https:// doi.org/10.3390/electronics13061145pt_PT
dc.identifier.doi10.3390/electronics13061145pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5649
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationLaboratory of Robotics and Engineering Systems
dc.relationResearch Centre for Mathematics and Applications
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectWater-level measurementpt_PT
dc.subjectImage processingpt_PT
dc.subjectDeep learningpt_PT
dc.subjectQater stream channelpt_PT
dc.subjectVolcanic islandspt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleNoncontact Automatic Water-Level Assessment and Prediction in an Urban Water Stream Channel of a Volcanic Island Using Deep Learningpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLaboratory of Robotics and Engineering Systems
oaire.awardTitleResearch Centre for Mathematics and Applications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50009%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04674%2F2020/PT
oaire.citation.issue6pt_PT
oaire.citation.startPage1145pt_PT
oaire.citation.titleElectronicspt_PT
oaire.citation.volume13pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameSilva Mendonça
person.familyNameMostafa
person.familyNameMorgado-Dias
person.familyNameAzevedo
person.familyNameRavelo-García
person.familyNameNavarro-Mesa
person.givenNameFábio Rúben
person.givenNameSheikh Shanawaz
person.givenNameFernando
person.givenNameJoaquim
person.givenNameAntonio G.
person.givenNameJuan L.
person.identifier34497
person.identifier1622144
person.identifier285354
person.identifier.ciencia-id7F1E-8AE9-3098
person.identifier.ciencia-idEE14-BEB3-F82B
person.identifier.ciencia-id7B14-DF07-AA6D
person.identifier.ciencia-idD417-D5FC-FAC3
person.identifier.orcid0000-0002-5107-3248
person.identifier.orcid0000-0002-7677-0971
person.identifier.orcid0000-0001-7334-3993
person.identifier.orcid0000-0002-9060-7476
person.identifier.orcid0000-0002-8512-965X
person.identifier.orcid0000-0003-3860-3424
person.identifier.ridN-9228-2015
person.identifier.ridN-4211-2013
person.identifier.ridL-3633-2017
person.identifier.scopus-author-id55489640900
person.identifier.scopus-author-id9243995600
person.identifier.scopus-author-id9634488300
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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