  Floods can be very destructive causing heavy damage to life, property, and
livelihoods. Global climate change and the consequent sea-level rise have
increased the occurrence of extreme weather events, resulting in elevated and
frequent flood risk. Therefore, accurate and timely flood forecasting in
coastal river systems is critical to facilitate good flood management. However,
the computational tools currently used are either slow or inaccurate. In this
paper, we propose a Flood prediction tool using Graph Transformer Network
(FloodGTN) for river systems. More specifically, FloodGTN learns the
spatio-temporal dependencies of water levels at different monitoring stations
using Graph Neural Networks (GNNs) and an LSTM. It is currently implemented to
consider external covariates such as rainfall, tide, and the settings of
hydraulic structures (e.g., outflows of dams, gates, pumps, etc.) along the
river. We use a Transformer to learn the attention given to external covariates
in computing water levels. We apply the FloodGTN tool to data from the South
Florida Water Management District, which manages a coastal area prone to
frequent storms and hurricanes. Experimental results show that FloodGTN
outperforms the physics-based model (HEC-RAS) by achieving higher accuracy with
70% improvement while speeding up run times by at least 500x.
