Storm Surge Modeling, Bias Correction, Graph Neural Networks, Graph Convolution Networks

2026-04-22

AI Summary

This study addresses the significant uncertainty in storm surge numerical forecasting, which hinders accurate assessment of tropical cyclone threats to coastal regions. The authors propose StormNet, a spatiotemporal graph neural network that integrates graph convolution, graph attention, and LSTM modules, marking the first application of graph neural networks to storm surge bias correction. StormNet effectively captures the spatiotemporal dependencies among tide gauge stations while balancing physical interpretability and computational efficiency. Evaluated on Hurricane Idalia, the model reduces the root mean square error (RMSE) of 48-hour and 72-hour water level predictions by over 70% and 50%, respectively, substantially outperforming a pure LSTM baseline. Its high training efficiency further supports its suitability for operational real-time forecasting.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

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 temporal dependencies among water-level gauge stations. The model was trained using historical hurricane data from the U.S. Gulf Coast and evaluated on Hurricane Idalia (2023). Results demonstrate that StormNet can effectively reduce the root mean square error (RMSE) in water-level predictions by more than 70\% for 48-hour forecasts and above 50\% for 72-hour forecasts, as well as outperform a sequential LSTM baseline, particularly for longer prediction horizons. The model also exhibits low training time, enhancing its applicability in real-time operational forecasting systems. Overall, StormNet provides a computationally efficient and physically meaningful framework for improving storm surge prediction accuracy and reliability during extreme weather events.

Problem

• Storm Surge Modeling
• Bias Correction
• Graph Neural Networks
• Graph Convolution Networks

Innovation

• Graph Neural Networks
• Bias Correction
• Storm Surge Forecasting
• Graph Convolution Networks
• Spatio-temporal Modeling