Long-Lead-Time Typhoon Wave Prediction Using Data-Driven Models, Typhoon Parameters, and Geometric Effective Factors on the Northwest Coast of Taiwan

This study introduces an innovative long-lead-time prediction model for typhoon-induced waves through the back-propagation neural network (BPNN) method along Taiwan’s northwest coast, a region vulnerable to severe coastal hazards due to its exposure to frequent typhoons and ongoing offshore energy development. Utilizing data from 13 typhoons (2001–2024) at the Hsinchu buoy station, the model integrates nine parameters—including significant wave height, typhoon parameters (e.g., wind speed, central pressure), and novel geometric factors like topographic elevation—to enhance forecast accuracy. The proposed WVPDUG model, incorporating forward speed, movement direction, and topography, outperforms traditional approaches, achieving over 60% improvement in RMSE and CC for lead times up to 10 h. A knowledge extraction method (KEM) further unveils the neural network’s internal dynamics, offering unprecedented insight into parameter contributions. This research addresses a critical gap in long-term wave forecasting under complex topographic influences, providing a robust tool for early warning systems and coastal disaster mitigation in typhoon-prone regions.