Articles | Volume 26, issue 19
https://doi.org/10.5194/hess-26-5163-2022
https://doi.org/10.5194/hess-26-5163-2022
Research article
 | 
14 Oct 2022
Research article |  | 14 Oct 2022

A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion

Alexander Y. Sun, Peishi Jiang, Zong-Liang Yang, Yangxinyu Xie, and Xingyuan Chen

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Cited articles

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Short summary
High-resolution river modeling is of great interest to local governments and stakeholders for flood-hazard mitigation. This work presents a physics-guided, machine learning (ML) framework for combining the strengths of high-resolution process-based river network models with a graph-based ML model capable of modeling spatiotemporal processes. Results show that the ML model can approximate the dynamics of the process model with high fidelity, and data fusion further improves the forecasting skill.
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