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Hybrid modelling to improve operational wave forecasts by combining process-based and machine learning models
den Bieman, J.P.; de Ridder, M.P.; Irías Mata, M.; van Nieuwkoop, J.C.C. (2023). Hybrid modelling to improve operational wave forecasts by combining process-based and machine learning models. Appl. Ocean Res. 136: 103583. https://dx.doi.org/10.1016/j.apor.2023.103583
In: Applied Ocean Research. CML Publications/Elsevier: Southampton. ISSN 0141-1187; e-ISSN 1879-1549, meer
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| Trefwoord |
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| Author keywords |
Operational forecasting; Hybrid modelling; Machine learning; XGBoost; Spectral wave modelling; SWAN |
| Auteurs | | Top |
- den Bieman, J.P.
- de Ridder, M.P.
- Irías Mata, M.
- van Nieuwkoop, J.C.C.
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| Abstract |
Operational wave forecasting plays an important role in ensuring safe navigation and in the prediction of tidal windows for harbour approach channels. The underlying nearshore process-based wave models need to be accurate for a wide range of different conditions, from more common mild wave conditions to the occasional high energy (storm) conditions. In this work, an innovative hybrid modelling approach is proposed to improve the accuracy of operational wave forecasts. An operational wave model is combined with a machine learning model which is trained using wave measurements within the wave model domain. This hybrid modelling approach is applied to the Dutch North Sea, covering four major harbour approach channels.The final hybrid operational wave model results in a significant average error decrease compared to just the process-based model, amounting to 21.7% for the wave energy density and 25.3% for the wave direction. The error reduction for the spectral wave parameters is even larger, with a 33.3% smaller error in spectral wave height and a 38.8% smaller error in spectral wave period. As this approach is generically applicable to spectral wave models, it contains the potential for significant improvements in operational modelling. |
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