A New Graph-Based Deep Learning Model to Predict Flooding with Validation on a Case Study on the Humber River

Floods are one of the most lethal natural disasters. It is crucial to forecast timing and evolution these events create an advanced warning system allow for proper implementation preventive measures. This work introduced a new graph-based forecasting model, namely, graph neural network sample aggregate (GNN-SAGE), estimate river flooding. then validated proposed model in Humber River watershed Ontario, Canada. Using past precipitation stage data from reference neighboring stations, GNN-SAGE could flooding up 24 h ahead, improving its performance by average 18% compared with persistence 9% residual gated convolutional (GNN-ResGated), which were used as baselines. Furthermore, generated smaller errors than those reported current literature. The Shapley additive explanations (SHAP) revealed that prior station was significant factor all prediction intervals, seasonality being more influential longer-range forecasts. findings positioned cutting-edge solution flood valuable resource devising early flood-warning systems.