Deep Learning for Typhoon Wave Height and Spectra Simulation

Typhoon-induced waves significantly threaten marine transportation and safety, often leading to catastrophic marine disasters. Accurate wave simulations are vital for effective disaster prevention. However, traditional studies have primarily focused on significant wave height (SWH) and heavily relied on resource-intensive numerical simulations while often neglecting wave spectra, which are essential for understanding the distribution of wave energy across various frequencies and directions. Addressing this gap, our study introduces an LSTM-Self Attention-Dense model that comprehensively simulates both SWH and wave frequency spectra. The model was rigorously trained and validated on three years of global typhoon data and exhibited accuracy in forecasting both SWH and wave spectra. Furthermore, our analysis identifies optimal input data windows and underscores wind speed and central pressure as critical predictive features. This novel approach not only enhances marine risk assessment but also offers a swift and efficient forecasting tool for managing extreme weather events, thereby contributing to the advancement of disaster management strategies.