From Rainfall to Road-Network Functionality: Transferable Graph Forecasting for Flood-Disrupted Transportation Networks
The G-R2R framework addresses how extreme rainfall rapidly degrades road networks by learning a transferable method to predict this disruption across different, unseen transportation networks. It uses a graph-learning approach that incorporates regional context and dynamic rainfall data to forecast road functionality without needing retraining for new areas.