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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- Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al. 10.1038/s43017-023-00450-9
- Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning T. Bindas et al. 10.1029/2023WR035337
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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
- Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network X. Ying et al. 10.3390/math12101534
- 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
- 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
- Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
- Simulation of spring discharge using graph neural networks at Niangziguan Springs, China Y. Gai et al. 10.1016/j.jhydrol.2023.130079
- 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
- Better localized predictions with Out-of-Scope information and Explainable AI: One-Shot SAR backscatter nowcast framework with data from neighboring region Z. Li & I. Demir 10.1016/j.isprsjprs.2023.11.021
- Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture S. Topp et al. 10.1029/2022WR033880
20 citations as recorded by crossref.
- Runoff Prediction Based on Dynamic Spatiotemporal Graph Neural Network S. Yang et al. 10.3390/w15132463
- 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
- Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al. 10.1038/s43017-023-00450-9
- Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning T. Bindas et al. 10.1029/2023WR035337
- Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. 10.1016/j.jhydrol.2024.131438
- Multisite algal bloom predictions in a lake using graph attention networks N. Kim et al. 10.4491/eer.2023.210
- A novel machine learning-based framework for the water quality parameters prediction using hybrid long short-term memory and locally weighted scatterplot smoothing methods A. Dodig et al. 10.2166/hydro.2024.273
- Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models S. Kim et al. 10.1016/j.wroa.2024.100228
- 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
- Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network X. Ying et al. 10.3390/math12101534
- 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
- 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
- Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
- Simulation of spring discharge using graph neural networks at Niangziguan Springs, China Y. Gai et al. 10.1016/j.jhydrol.2023.130079
- 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
- Better localized predictions with Out-of-Scope information and Explainable AI: One-Shot SAR backscatter nowcast framework with data from neighboring region Z. Li & I. Demir 10.1016/j.isprsjprs.2023.11.021
1 citations as recorded by crossref.
Latest update: 17 Jul 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...
