Improving accuracy on wave height estimation through machine learning techniques

Gracia, S. ; Olivito, J. (Universidad de Zaragoza) ; Resano, J. (Universidad de Zaragoza) ; Martin-del-Brio, B. (Universidad de Zaragoza) ; de Alfonso, M. ; Álvarez, E.

Resumen: 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.

Idioma: Inglés

DOI: 10.1016/j.oceaneng.2021.108699

Año: 2021

Publicado en: OCEAN ENGINEERING 236 (2021), 108699 [13 pp]
ISSN: 0029-8018

Factor impacto JCR: 4.372 (2021)

Categ. JCR: ENGINEERING, OCEAN rank: 4 / 16 = 0.25 (2021) - Q1 - T1
Categ. JCR: OCEANOGRAPHY rank: 6 / 66 = 0.091 (2021) - Q1 - T1
Categ. JCR: ENGINEERING, MARINE rank: 3 / 16 = 0.188 (2021) - Q1 - T1
Categ. JCR: ENGINEERING, CIVIL rank: 34 / 138 = 0.246 (2021) - Q1 - T1

Factor impacto CITESCORE: 6.5 - Environmental Science (Q1) - Engineering (Q1)

Factor impacto SCIMAGO: 1.381 - Ocean Engineering (Q1) - Environmental Engineering (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA-ESF/T58-20R
Financiación: info:eu-repo/grantAgreement/ES/DGA/T27-17R
Financiación: info:eu-repo/grantAgreement/ES/MINECO-AEI-ERDF/PID2019-105660RB-C21
Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/TIN2017-88841-R
Financiación: info:eu-repo/grantAgreement/ES/MINECO/RTC-2015-3358-5
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TIN2016-76635-C2-1-R

Tipo y forma: Article (Published version)

Área (Departamento): Área Arquit.Tecnología Comput. (Dpto. Informát.Ingenie.Sistms.)
Área (Departamento): Área Tecnología Electrónica (Dpto. Ingeniería Electrón.Com.)