Maximum Wave Height Prediction Based on Buoy Data: Application of LightGBM and TCN-BiGRU

Extreme sea conditions caused by tropical cyclones pose significant risks to coastal safety, infrastructure, and ecosystems. Although existing models have advanced in predicting Significant Wave Height (SWH), their performance in predicting Maximum Wave Height (MWH) remains limited, particularly in capturing rapid wave fluctuations and localized meteorological dynamics. This study proposes a novel MWH prediction framework that integrates high-resolution buoy observations with deep learning. A moored buoy deployed in the Qiongzhou Strait provides precise nearshore observations, compensating for limitations in reanalysis datasets. Light Gradient Boosting Machine (LightGBM) is employed for key feature selection, and a hybrid Bidirectional Temporal Convolutional Network-Bidirectional Gated Recurrent Unit (BiTCN-BiGRU) model is constructed to capture both short- and long-term temporal dependencies. The results show that BiTCN-BiGRU outperforms BiGRU, reducing MAE by 6.11%, 5.41%, and 14.09% for 1-h, 3-h, and 6-h forecasts. This study also introduces the Time Distortion Index (TDI) into MWH prediction as a novel metric for evaluating temporal alignment. This study offers valuable insights for disaster warning, coastal protection, and risk mitigation under extreme marine conditions.