Prediction and reconstruction of ocean wave heights based on bathymetric data using LSTM neural networks

Abstract

Since climate change impacts threaten the coastal regions of the North Sea, consistent sea state time series are essential for building coastal protection or offshore structures. Vast gaps in buoy data, caused by false measurements or maintenance periods, require reconstruction and forecasting of ocean waves. Morphodynamic changes of the Ebb-Tidal Delta (ETD) sandbanks exert a huge impact on the local wave climate off the island of Norderney, Germany. Therefore, the objective of this paper is to develop a machine learning model based on Long Short-Term Memory (LSTM) neural networks for reconstruction and short- and long-term prediction of nearshore significant wave height (SWH), integrating bathymetric data for the first time. Time series of sea state and weather data of adjacent buoys as well as bathymetric data of the ETD sandbanks from 2004 to 2017 were used and the networks were tuned with Bayesian hyperparameter optimization. Including the bathymetries improved the performance of the LSTM for SWH reconstruction, short-, and long-term predictions by 16.7%, 7.4–11.7%, and 8.8–9.1% in terms of RMSE, respectively. The LSTM outperformed deep feed-forward neural networks and other state-of-the-art machine learning algorithms. With an RMSE of 0.069 m, a parallel LSTM structure (P-LSTM) is the proposed method for SWH reconstruction.