Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 19
Articles | Volume 26, issue 19
https://doi.org/10.5194/hess-26-5163-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-5163-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 19
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

Viewed

Total article views: 8,623 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
6,404 2,125 94 8,623 325 96 113
  • HTML: 6,404
  • PDF: 2,125
  • XML: 94
  • Total: 8,623
  • Supplement: 325
  • BibTeX: 96
  • EndNote: 113
Views and downloads (calculated since 25 Apr 2022)
Cumulative views and downloads (calculated since 25 Apr 2022)

Viewed (geographical distribution)

Total article views: 8,623 (including HTML, PDF, and XML) Thereof 8,358 with geography defined and 265 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 25 Nov 2025
Download
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.
Read more
Share