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Deep Learning-Based Wave Overtopping Prediction
dc.contributor.author | Alvarellos, Alberto | |
dc.contributor.author | Figuero, A. | |
dc.contributor.author | Rodríguez-Yáñez, Santiago | |
dc.contributor.author | Sande, José | |
dc.contributor.author | Peña González, Enrique | |
dc.contributor.author | Rosa-Santos, Paulo | |
dc.contributor.author | Rabuñal, Juan R. | |
dc.date.accessioned | 2024-05-24T15:38:42Z | |
dc.date.available | 2024-05-24T15:38:42Z | |
dc.date.issued | 2024-03-20 | |
dc.identifier.citation | Alvarellos, A.; Figuero, A.; Rodríguez-Yáñez, S.; Sande, J.; Peña, E.; Rosa-Santos, P.; Rabuñal, J. Deep Learning-Based Wave Overtopping Prediction. Appl. Sci. 2024, 14(6), 2611. https://doi.org/10.3390/app14062611 | es_ES |
dc.identifier.issn | 2076-3417 | |
dc.identifier.uri | http://hdl.handle.net/2183/36619 | |
dc.description.abstract | [Abstract]: This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video-recording infrastructure was installed to monitor overtopping events from 2015 to 2022, identifying 3709 overtopping events. The data collected were merged with actual and predicted data for the sea state and weather conditions during the overtopping events, creating three datasets. We used these datasets to create several machine learning models to predict whether an overtopping event would occur based on sea state and weather conditions. The final models achieved a high accuracy level during the training and testing stages: 0.81, 0.73, and 0.84 average accuracy during training and 0.67, 0.48, and 0.86 average accuracy during testing, respectively. The results of this study have significant implications for port safety and efficiency, as wave overtopping events can cause disruptions and potential damage. Using deep learning techniques for overtopping prediction can help port managers take preventative measures and optimize operations, ultimately improving safety and helping to minimize the economic impact that overtopping events have on the port’s activities. | es_ES |
dc.description.sponsorship | This research was funded by the Spanish Ministry of Science and Innovation [grant number PID2020-112794RB-I00, funded by MCIN/AEI/10.13039/501100011033]. The authors would like to thank the Port Authority of A Coruña (Spain) for their availability, collaboration, interest and promotion of research in port engineering. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-112794RB-I00/ES/HERRAMIENTAS PREDICTIVAS PARA LA TOMA DE DECISIONES EN LA GESTION PORTUARIA BASADAS EN MACHINE LEARNING. INCLUSION DE CRITERIOS DE PERMANENCIA EN ATRAQUE, ONDA LARGA Y REBASE | es_ES |
dc.relation.uri | https://doi.org/10.3390/app14062611 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights | © 2024 the authors | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Machine learning | es_ES |
dc.subject | Neural networks | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | Wave overtopping prediction | es_ES |
dc.subject | Port management | es_ES |
dc.subject | Port security | es_ES |
dc.title | Deep Learning-Based Wave Overtopping Prediction | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Applied Sciences | es_ES |
UDC.volume | 14 | es_ES |
UDC.issue | 6: 2611 | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 21 | es_ES |
dc.identifier.doi | 10.3390/app14062611 |
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