000109075 001__ 109075
000109075 005__ 20240122154815.0
000109075 0247_ $$2doi$$a10.1016/j.oceaneng.2021.108699
000109075 0248_ $$2sideral$$a125488
000109075 037__ $$aART-2021-125488
000109075 041__ $$aeng
000109075 100__ $$aGracia, S.
000109075 245__ $$aImproving accuracy on wave height estimation through machine learning techniques
000109075 260__ $$c2021
000109075 5060_ $$aAccess copy available to the general public$$fUnrestricted
000109075 5203_ $$aEstimatabion 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.
000109075 536__ $$9info:eu-repo/grantAgreement/ES/DGA-ESF/T58-20R$$9info:eu-repo/grantAgreement/ES/DGA/T27-17R$$9info:eu-repo/grantAgreement/ES/MINECO-AEI-ERDF/PID2019-105660RB-C21$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/TIN2017-88841-R$$9info:eu-repo/grantAgreement/ES/MINECO/RTC-2015-3358-5$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2016-76635-C2-1-R
000109075 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000109075 590__ $$a4.372$$b2021
000109075 592__ $$a1.381$$b2021
000109075 594__ $$a6.5$$b2021
000109075 591__ $$aENGINEERING, OCEAN$$b4 / 16 = 0.25$$c2021$$dQ1$$eT1
000109075 593__ $$aOcean Engineering$$c2021$$dQ1
000109075 591__ $$aOCEANOGRAPHY$$b6 / 66 = 0.091$$c2021$$dQ1$$eT1
000109075 593__ $$aEnvironmental Engineering$$c2021$$dQ1
000109075 591__ $$aENGINEERING, MARINE$$b3 / 16 = 0.188$$c2021$$dQ1$$eT1
000109075 591__ $$aENGINEERING, CIVIL$$b34 / 138 = 0.246$$c2021$$dQ1$$eT1
000109075 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000109075 700__ $$0(orcid)0000-0002-7752-8714$$aOlivito, J.$$uUniversidad de Zaragoza
000109075 700__ $$0(orcid)0000-0002-7532-2720$$aResano, J.$$uUniversidad de Zaragoza
000109075 700__ $$0(orcid)0000-0002-3643-2847$$aMartin-del-Brio, B.$$uUniversidad de Zaragoza
000109075 700__ $$ade Alfonso, M.
000109075 700__ $$aÁlvarez, E.
000109075 7102_ $$15007$$2035$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Arquit.Tecnología Comput.
000109075 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000109075 773__ $$g236 (2021), 108699 [13 pp]$$pOcean eng.$$tOCEAN ENGINEERING$$x0029-8018
000109075 8564_ $$s8720363$$uhttps://zaguan.unizar.es/record/109075/files/texto_completo.pdf$$yVersión publicada
000109075 8564_ $$s2552119$$uhttps://zaguan.unizar.es/record/109075/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000109075 909CO $$ooai:zaguan.unizar.es:109075$$particulos$$pdriver
000109075 951__ $$a2024-01-22-15:36:17
000109075 980__ $$aARTICLE