109075 20240122154815.0 doi 10.1016/j.oceaneng.2021.108699 sideral 125488 ART-2021-125488 eng Gracia, S. Improving accuracy on wave height estimation through machine learning techniques 2021 Access copy available to the general public Unrestricted Estimatabion of wave agitation plays a key role in predicting natural disasters, path optimization and secure harbor operation. The Spanish agency Puertos del Estado (PdE) has several oceanographic measure networks equipped with sensors for different physical variables, and manages forecast systems involving numerical models. In recent years, there is a growing interest in wave parameter estimation by using machine learning models due to the large amount of oceanographic data available for training, as well as its proven efficacy in estimating physical variables. In this study, we propose to use machine learning techniques to improve the accuracy of the current forecast system of PdE. We have focused on four physical wave variables: spectral significant height, mean spectral period, peak period and mean direction of origin. Two different machine learning models have been explored: multilayer perceptron and gradient boosting decision trees, as well as ensemble methods that combine both models. These models reduce the error of the predictions of the numerical model by 36% on average, demonstrating the potential gains of combining machine learning and numerical models. info:eu-repo/grantAgreement/ES/DGA-ESF/T58-20R info:eu-repo/grantAgreement/ES/DGA/T27-17R info:eu-repo/grantAgreement/ES/MINECO-AEI-ERDF/PID2019-105660RB-C21 info:eu-repo/grantAgreement/ES/MINECO-FEDER/TIN2017-88841-R info:eu-repo/grantAgreement/ES/MINECO/RTC-2015-3358-5 info:eu-repo/grantAgreement/ES/MINECO/TIN2016-76635-C2-1-R info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 4.372 2021 ENGINEERING, OCEAN 4 / 16 = 0.25 2021 Q1 T1 OCEANOGRAPHY 6 / 66 = 0.091 2021 Q1 T1 ENGINEERING, MARINE 3 / 16 = 0.188 2021 Q1 T1 ENGINEERING, CIVIL 34 / 138 = 0.246 2021 Q1 T1 1.381 2021 Ocean Engineering 2021 Q1 Environmental Engineering 2021 Q1 6.5 2021 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Olivito, J. Universidad de Zaragoza (orcid)0000-0002-7752-8714 Resano, J. Universidad de Zaragoza (orcid)0000-0002-7532-2720 Martin-del-Brio, B. Universidad de Zaragoza (orcid)0000-0002-3643-2847 de Alfonso, M. Álvarez, E. 5007 035 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Arquit.Tecnología Comput. 5008 785 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Tecnología Electrónica 236 (2021), 108699 [13 pp] Ocean eng. OCEAN ENGINEERING 0029-8018 8720363 http://zaguan.unizar.es/record/109075/files/texto_completo.pdf Versión publicada 2552119 http://zaguan.unizar.es/record/109075/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:109075 articulos driver 2024-01-22-15:36:17 ARTICLE