Advancing Storm Surge Forecasting from Scarce Observation Data: A Causal-Inference Based Spatio-Temporal Graph Neural Network Approach

Rapid and precise forecasting of storm surge in coastal regions is crucial for ensuring safety of coastal communities’ life and property. Yet, learning a data-driven forecasting model from observation data such as gauges and post-event reconnaissance remains challenging, due to the observation data scarcity and the real-world complexity. Recently, deep learning has received increasing attention, but existing deep learning approaches solely focus on individual site scenarios, ignoring the value of information contained in neighboring sites’ observations. In this study, we propose to integrate graph neural networks (GNN) and gated recurrent unit (GRU) to capture the spatial and temporal storm surge dependencies across multiple observation stations. GNN provides the unique capability to model non-Euclidean complex spatial relationship across observation stations, while GRU enhances the data efficiency of temporal dependency modeling. To account for the effect of complex coastline topography, the Liang-Kleeman information flow theory is employed to establish a causal-inference based graph edge scheme connecting multiple observation stations. The Causal-inference based Spatio-Temporal Graph Neural Network (CSTGNN) were trained and evaluated on 13-year observation data from 4 observation stations along Florida coastline. Experiments affirm the competence of CSTGNN, which outperformed six commonly used competitive baselines across different metrics and observation stations, under lead times up to six hours. Furthermore, benefits of capturing the spatial dependency and leveraging causal inference are also comprehensively examined. To conclude, we believe that this novel spatio-temporal forecasting framework can result in enhanced coastal resilience by its improved storm surge forecasting capability.