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Floods can be very destructive causing heavy damage to life, property, andlivelihoods. Global climate change and the consequent sea-level rise haveincreased the occurrence of extreme weather events, resulting in elevated andfrequent flood risk. Therefore, accurate and timely flood forecasting incoastal river systems is critical to facilitate good flood management. However,the computational tools currently used are either slow or inaccurate. In thispaper, we propose a Flood prediction tool using Graph Transformer Network(FloodGTN) for river systems. More specifically, FloodGTN learns thespatio-temporal dependencies of water levels at different monitoring stationsusing Graph Neural Networks (GNNs) and an LSTM. It is currently implemented toconsider external covariates such as rainfall, tide, and the settings ofhydraulic structures (e.g., outflows of dams, gates, pumps, etc.) along theriver. We use a Transformer to learn the attention given to external covariatesin computing water levels. We apply the FloodGTN tool to data from the SouthFlorida Water Management District, which manages a coastal area prone tofrequent storms and hurricanes. Experimental results show that FloodGTNoutperforms the physics-based model (HEC-RAS) by achieving higher accuracy with70% improvement while speeding up run times by at least 500x.

Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates