Huge waves caused by typhoons often induce severe disasters along coastal areas, making the effective prediction of typhoon-induced waves a crucial research issue for researchers. In recent years, the development of the Internet of Underwater Things (IoUT) has rapidly increased the prediction of oceanic environmental disasters. Past studies have utilized meteorological data and feedforward neural networks (e.g., BPNN) with static network structures to establish short lead time (e.g., 1 h) typhoon wave prediction models for the coast of Taiwan. However, sufficient lead time for prediction remains essential for preparedness, early warning, and response to minimize the loss of lives and properties during typhoons. The aim of this research is to construct a novel long lead time typhoon-induced wave prediction model using Long Short-Term Memory (LSTM), which incorporates a dynamic network structure. LSTM can capture long-term information through its recurrent structure and selectively retain necessary signals using memory gates. Compared to earlier studies, this method extends the prediction lead time and significantly improves the learning and generalization capability, thereby enhancing prediction accuracy markedly.

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.