Seamless Storm Surge Prediction Using a Surrogate Hydrodynamic Model Based on Long Short-Term Memory Networks

Accurate storm surge prediction is essential for reducing the risks associated with extreme sea levels and for supporting early warning and preventive measures. Physically based numerical models continue to improve in skill and resolution, but their high computational cost limits their use in large ensembles and long-term scenario analyses. Recent advances in machine learning offer a complementary pathway for efficient storm surge forecasting. Here, a machine-learning framework is developed, calibrated, and validated to predict extreme sea levels in the North Sea and Baltic Sea. The model is based on 58 years of spatially distributed wind data and uses a Long Short-Term Memory (LSTM) architecture to capture the temporal dynamics driving water level variability. Compared to traditional physically based hydrodynamic models, the machine-learning approach requires only a fraction of the computational resources, enabling rapid probabilistic and large-ensemble forecasts across large domains and extended time periods. This efficiency is particularly valuable for climate change research, where large ensembles are generally needed to address the combined uncertainty of climate and hydrodynamic models but remain computationally prohibitive using conventional approaches. By providing a scalable and resource-efficient alternative, this framework enables consistent storm surge prediction across timescales ranging from short-term forecasting to long-term climate projections over decades.