Machine Learning Techniques for Predicting Typhoon‐Induced Storm Surge Using a Hybrid Wind Field - Changyu Su; Bishnupriya Sahoo; Miaohua Mao; Meng Xia (Journal of Geophysical Research: Machine Learning and Computation, 2025)
Abstract Accurate and timely storm surge prediction is critical information in coastal zone management and risk reduction strategies. The Bohai Sea, a semi‐enclosed bay in the Northwest Pacific that used to be less prone to typhoon disasters, has been witnessing a paradigm shift in typhoon activities in the recent past. Since there have been limited typhoon‐induced storm surges in the Bohai Sea, an innovative prediction system is warranted to address frequent and intense typhoon‐induced impacts. Four Machine Learning (ML) models (Long Short‐Term Memory (LSTM), Convolutional Neural Networks (CNN), CNN‐LSTM, and ConvLSTM) were built to predict storm surges and significantly improve prediction when combined with a three‐dimensional Finite Volume Community Ocean Model (FVCOM), that is, FVCOM‐ML. In this study, the FVCOM‐ML model was driven by a hybrid wind field that superimposed the Holland wind and the reanalysis wind field. The ML models were trained via Advanced Circulation Model simulations to compensate for the limited in‐situ observations. The prediction performances were analyzed for both spatial (e.g., single and multiple sites) and temporal (e.g., single and multiple steps) scale variability. ML is trained to overcome the residual error of the FVCOM, effectively reducing the inherent uncertainty of traditional methods. FVCOM‐ML offers a significant advantage over standalone FVCOM or ML while better incorporating realistic physical constraints and improving the accuracy of storm surge forecasts.