Hybrid modelling to improve operational wave forecasts by combining process-based and machine learning models

Joost P. den Bieman, Menno P. de Ridder, Marisol Irías Mata, Joana C.C. van Nieuwkoop

Research output: Contribution to journalArticleScientificpeer-review


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.
Original languageEnglish
Article number103583
Number of pages11
JournalApplied Ocean Research
Publication statusPublished - 2023
Externally publishedYes


  • Operational forecasting
  • Hybrid modelling
  • Machine learning
  • XGBoost
  • Spectral wave modelling


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