Short-term prediction of storm surges in estuarine and coastal waters via a multipoint deep learning neural network with limited training samples - Zhuo Zhang; Lu Zhang; Songshan Yue; Dong Zhang; Zhaoyuan Yu; Di Hu; Peng Chen (International Journal of Digital Earth, 2025)
Accurate estimation of extreme sea levels caused by storm surges is critical for coastal engineering, particularly during typhoon seasons. Data-driven approaches have emerged as efficient tools for storm surge prediction. This study presents the development of deep learning neural network models to predict storm surge levels in the estuarine and coastal waters of the Pearl River Estuary, China, using typhoon characteristics and previous surge data. Unlike traditional single-point neural network models that focus on individual tide stations, this study introduces multipoint models and incorporates a convolutional layer to extract spatial features from tide levels at neighboring stations. Comparative results reveal that multipoint models significantly increase prediction accuracy by integrating spatiotemporal information from surrounding stations. This advantage is particularly evident when forecasting lead times exceed six hours, where multipoint models demonstrate superior accuracy and stability compared with single-point models. Furthermore, this study evaluates the impact of limited training sample sizes on prediction accuracy, offering valuable insights into the data requirements for robust model training. The findings highlight the potential of using multipoint neural network models as effective tools for storm surge prediction, offering increased accuracy and contributing to enhanced coastal risk management and decision-making in the face of extreme weather events.