A heterogeneous pixel-level graph attention transformer reaches NSE up to 0.97 for hourly flood prediction on two Midwest basins and scales to 64 GPUs.
Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks
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abstract
The frequency and impact of floods are expected to increase due to climate change. It is crucial to predict streamflow, consequently flooding, in order to prepare and mitigate its consequences in terms of property damage and fatalities. This paper presents a Graph Convolutional GRUs based model to predict the next 36 hours of streamflow for a sensor location using the upstream river network. As shown in experiment results, the model presented in this study provides better performance than the persistence baseline and a Convolutional Bidirectional GRU network for the selected study area in short-term streamflow prediction.
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HydroGAT: Distributed Heterogeneous Graph Attention Transformer for Spatiotemporal Flood Prediction
A heterogeneous pixel-level graph attention transformer reaches NSE up to 0.97 for hourly flood prediction on two Midwest basins and scales to 64 GPUs.