Topology enables learning-based hydrodynamic prediction of the global river system
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The paper introduces GraphRiverCast (GRC), a topology-informed foundation model that predicts daily multivariate hydrodynamics across the global river network using sparse gauge data. By leveraging river connectivity and energetics, GRC outperforms both physics-based and traditional machine-learning models, maintaining skill over long lead times and generalizing to ungauged reaches and finer-resolution networks. The approach demonstrates how incorporating system structure can improve predictions in observation-limited Earth systems.
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The paper introduces AmazonSWE, a dataset covering over 19,000 river sections in the Amazon basin for 10 years, combining satellite altimetry and in‑situ gauge data to enable large‑scale spatiotemporal graph imputation. The authors highlight the extreme sparsity of observations—less than 1% of sections per day—and the directed acyclic topology of river networks, which challenge existing imputation methods. They propose a bidirectional selective state‑space model that samples connected subgraphs and uses topology‑aware positional encodings, achieving 18–39% lower RMSE than the current state‑of‑the‑art SWOT‑based approach while providing predictions for all river sections.
Emergency managers need to know where floodwater is, how deep it is, and how it will change over the coming hours across an entire river basin. During a flood, however, real-time measurements come fro...