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Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks

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arxiv 2107.07039 v1 pith:ZW3UELIJ submitted 2021-07-07 cs.LG eess.SP

Muhammed Sit , Bekir Demiray , Ibrahim Demir This is my paper · ORCID
classification cs.LGeess.SP
keywords streamflowconvolutionalgraphmodelnetworkpredictpredictionshort-term
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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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Cited by 1 Pith paper

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  1. HydroGAT: Distributed Heterogeneous Graph Attention Transformer for Spatiotemporal Flood Prediction

    cs.LG 2025-09 conditional novelty 6.0 of 10

    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.

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