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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Oct 2022
Research article |
| 14 Oct 2022
| 14 Oct 2022
A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion
Bureau of Economic Geology, The University of Texas at Austin, Austin,
TX, USA
Peishi Jiang
Pacific Northwest National Laboratory, Richland, WA, USA
Zong-Liang Yang
Department of Geological Sciences, The University of Texas at Austin,
Austin, TX, USA
Yangxinyu Xie
Department of Computer Science, The University of Texas at Austin,
Austin, TX, USA
now at: The Department of Statistics and Data Science at The University
of Pennsylvania, Pennsylvania, USA
Xingyuan Chen
Pacific Northwest National Laboratory, Richland, WA, USA
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- Hydrogen jet and diffusion modeling by physics-informed graph neural network X. Zhang et al. 10.1016/j.rser.2024.114898
- A spatiotemporal graph convolution-based model for daily runoff prediction in a river network with non-Euclidean topological structure L. Deng et al. 10.1007/s00477-022-02352-6
- Great lakes basin model based on physical flow and Data-Driven Y. Huang et al. 10.1088/1742-6596/2865/1/012019
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- A framework on utilizing of publicly availability stream gauges datasets and deep learning in estimating monthly basin-scale runoff in ungauged regions M. Le et al. 10.1016/j.advwatres.2024.104694
- A review of hybrid deep learning applications for streamflow forecasting K. Ng et al. 10.1016/j.jhydrol.2023.130141
- Multi-step ahead probabilistic forecasting of multiple hydrological variables for multiple stations Z. Zhang et al. 10.1016/j.jhydrol.2023.129094
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- A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. 10.5194/hess-28-2871-2024
- River reach-level machine learning estimation of nutrient concentrations in Great Britain C. Tso et al. 10.3389/frwa.2023.1244024
- Graph neural network method for the intelligent selection of river system D. Wang & H. Qian 10.1080/10106049.2023.2252762
- Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision Q. Yang et al. 10.1016/j.compag.2024.108911
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- Enhancing hydrological data completeness: A performance evaluation of various machine learning techniques using probabilistic fusion imputer with neural networks for streamflow data reconstruction G. Arathy Nair et al. 10.1016/j.jhydrol.2024.131583
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Latest update: 22 Nov 2024
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.
High-resolution river modeling is of great interest to local governments and stakeholders for...
