Short-term wave forecasts using gated recurrent unit model

Yuval Yevnin, Shir Chorev, Ilan Dukan, Yaron Toledo

Research output: Contribution to journalArticlepeer-review


Short-term ocean waves forecasting requires a high degree of skill and knowledge, as one should observe the available model forecast and real-time measurement and reach a combined estimation. This paper presents a deep learning model providing a short-term wave height prediction derived from recent in-situ measurements and an available mid-range forecast. The model is based of a gated recurrent unit, which is common in time-series forecasting. The model is able to improve significant wave height RMSE by as much as 76% for 1 h forecasts and converge to ∼12% improvement for forecasts over 7 h. The model is also shown to be easily transferable to another station and achieves good performance without further training in a ”zero-shot” learning process. This model can prove valuable to various off-shore operations, allowing for data-driven decision making instead of skilled human operator and experience-based evaluation.

Original languageEnglish
Article number113389
JournalOcean Engineering
StatePublished - 15 Jan 2023


  • Data-based wave forecasting
  • Machine learning
  • Numerical wave forecasting models
  • Short-term wave forecast
  • Wave buoy measurements

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Ocean Engineering


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