Flood Forecasting with Graph Neural Networks
Due to climate change, riverine floods have become increasingly common. Forecasting them requires accurate discharge predictions. In this regard, deep learning methods recently started outperforming classical hydrolog- ical modeling techniques based on differential equations. The current state-the-art approaches treat forecasting at spatially distributed gauge stations as isolated problems. However, incorporating the known river network topology into the model has the potential to leverage the physical relationships between stations. Thus, we propose modeling river discharge for a network of gauging stations with a Graph Neural Network (GNN). To assess the benefit of relating stations to each other, we compare the forecasting performance achieved by different adjacency definitions: no adjacency at all, which is equivalent to existing approaches; binary adja- cency of nearest up-/downstream stations; weighted adjacency according to physical relationships like stream length between stations; and learned adjacency via joint parameterization. Our results show that the model does not benefit from the river network topology information, regardless of the number of layers. The learned edge weights correlate with neither of the static definitions and exhibit no regular pattern. Furthermore, a worst- case analysis shows that the GNN struggles to predict sudden discharge spikes. In employing the Gradient Flow Framework (GRAFF), we find that parameter sharing across layers does not hurt model performance and that a mixture of attractive and repulsive forces act on vertex representations in the latent space of the GNN.