StormNet: Improving storm surge predictions with a GNN-based spatio-temporal offset forecasting model
Storm surge forecasting remains a critical challenge in mitigating the impacts of tropical cyclones on coastal regions, particularly given recent trends of rapid intensification and increasing nearshore storm activity. Traditional high fidelity numerical models such as ADCIRC, while robust, are often hindered by inevitable uncertainties arising from various sources. To address these challenges, this study introduces StormNet, a spatio-temporal graph neural network (GNN) designed for bias correction of storm surge forecasts. StormNet integrates graph convolutional (GCN) and graph attention (GAT) mechanisms with long short-term memory (LSTM) components to capture complex spatial and tempora...
| Authors | Noujoud Nader, Stefanos Giaremis, Clint Dawson et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 April 2026 |
| Last updated at source | 23 April 2026 |
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