Cristin-resultat-ID: 1864813
Sist endret: 11. januar 2021, 14:08
NVI-rapporteringsår: 2020
Resultat
Vitenskapelig artikkel
2020

Multi-step-ahead forecasting of wave conditions based on a physics-based machine learning (PBML) model for marine operations

Bidragsytere:
  • Mengning Wu
  • Christos Stefanakos og
  • Zhen Gao

Tidsskrift

Journal of Marine Science and Engineering (JMSE)
ISSN 2077-1312
e-ISSN 2077-1312
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2020
Publisert online: 2020
Volum: 8
Hefte: 12
Sider: 1 - 24
Artikkelnummer: 992
Open Access

Importkilder

Scopus-ID: 2-s2.0-85097833395

Beskrivelse Beskrivelse

Tittel

Multi-step-ahead forecasting of wave conditions based on a physics-based machine learning (PBML) model for marine operations

Sammendrag

Short-term wave forecasts are essential for the execution of marine operations. In this paper, an efficient and reliable physics-based machine learning (PBML) model is proposed to realize the multi-step-ahead forecasting of wave conditions (e.g., significant wave height Hs and peak wave period Tp). In the model, the primary variables in physics-based wave models (i.e., the wind forcing and initial wave boundary condition) are considered as inputs. Meanwhile, a machine learning algorithm (artificial neural network, ANN) is adopted to build an implicit relation between inputs and forecasted outputs of wave conditions. The computational cost of this data-driven model is obviously much lower than that of the differential-equation based physical model. A ten-year (from 2001 to 2010) dataset of every three hours at the North Sea center was used to assess the model performance in a small domain. The result reveals high reliability for one-day-ahead Hs forecasts, while that of Tp is slightly lower due to the weaker implicit relationships between the data. Overall, the PBML model can be conceived as an efficient tool for the multi-step-ahead forecasting of wave conditions, and thus has great potential for furthering assist decision-making during the execution of marine operations.

Bidragsytere

Mengning Wu

  • Tilknyttet:
    Forfatter
    ved Institutt for marin teknikk ved Norges teknisk-naturvitenskapelige universitet
Aktiv cristin-person

Christos Stefanakos

  • Tilknyttet:
    Forfatter
    ved Fiskeri og ny biomarin industri ved SINTEF Ocean

Zhen Gao

  • Tilknyttet:
    Forfatter
    ved Institutt for marin teknikk ved Norges teknisk-naturvitenskapelige universitet
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