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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63 citations as recorded by crossref.
- Runoff Prediction Based on Dynamic Spatiotemporal Graph Neural Network S. Yang et al.
- Multisite algal bloom predictions in a lake using graph attention networks N. Kim et al.
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- Hybrid modeling for daily streamflow forecasting: A study over the contiguous United States F. Zeng et al.
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- Great lakes basin model based on physical flow and Data-Driven Y. Huang et al.
- Dynamic Graph Transformer with Spatio-Temporal Attention for Streamflow Forecasting B. Li et al.
- A heterogeneous multi-graph spatio-temporal network for runoff forecasting X. Zhou et al.
- Using tide for rainfall runoff simulation with feature projection and reversible instance normalization Z. Fang et al.
- A review of hybrid deep learning applications for streamflow forecasting K. Ng et al.
- Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning T. Bindas et al.
- Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al.
- Dynamic hydrological flow prediction with self-iterative spatiotemporal graph neural network: Modeling long- and short-period topological dynamics L. Xue & Y. Zhu
- A Review of the Advances and Emerging Approaches in Hydrological Forecasting: From Traditional to AI-Powered Models K. Robles et al.
- Knowledge-guided graph machine learning improves corn yield mapping in the U.S. Midwest J. Yang et al.
- Knowledge‐Guided Machine Learning for Global Change Ecology Research Z. Jin et al.
- Toward transparent groundwater contamination risk forecasting: Integrating causal discovery and Bayesian graph neural networks Y. Zhu & Q. Liu
- 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.
- Unstructured mesh-based graph neural networks for estimating the spatiotemporal distribution of a human-induced chemical in freshwater S. Kim et al.
- Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision Q. Yang et al.
- Exploring a process-aware spatiotemporal graph-based surrogate for integrated urban drainage simulation B. Yin et al.
- Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network X. Ying et al.
- Hybrid process-based and deep learning for river nutrient prediction under limited monitoring data J. Tang et al.
- Exploring viable approaches for long-term seasonal streamflow forecasting under different forcing mechanisms G. Zuo et al.
- Enhancing hydrological simulation and climate change impact assessment for the Poyang Lake Region, China: A novel hybrid SWAT-GCN-BiLSTM framework X. Zheng et al.
- 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.
- Automatic analysis method for topological relationship between river network and hydrological stations Y. Wang et al.
- A graph-based machine learning framework for river water quality management under data limitations S. Choi et al.
- A GNN routing module is all you need for LSTM Rainfall–Runoff models H. Mosaffa et al.
- 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
- Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al.
- Multi-scale dynamic spatiotemporal graph attention network for forecasting karst spring discharge R. Zhou
- 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.
- Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models S. Kim et al.
- Temporal-relational graph neural network for nearshore seawater quality parameters multivariate multi-step prediction and correlation modelling Q. Zhu et al.
- Role of river network information in streamflow prediction using graph wavenet and entity-aware long short-term memory models H. Seo et al.
- Convolutional Graph Neural Network with Novel Loss Strategies for Daily Temperature and Precipitation Statistical Downscaling over South China W. Yan et al.
- Enhancing Streamflow Prediction Using Cutting-edge Deep Learning Models and Seasonal-Trend Decomposition Y. Jia et al.
- An efficient parallel runoff forecasting model for capturing global and local feature information Y. Hong et al.
- A graph neural network embedded with heat kernel for multistep forecasting spring discharge X. Deng et al.
- Improving watershed-scale daily nutrient simulation using a process-model-informed graph attention network with multi-source data integration W. Wang et al.
- A graph Fourier Kolmogorov-Arnold Network (G-FourierKAN) and its application to spring discharge simulation Y. Yin et al.
- Adaptive physics-informed graph convolutional network for flow prediction in the downstream river network of the dongjiang river Y. Liu et al.
- Simulation of spring discharge using graph neural networks at Niangziguan Springs, China Y. Gai et al.
- 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.
- Enhanced water quality prediction by LSTM and graph attention network (L-GAT): An analytical study of the Pearl River Basin Y. Liu et al.
- A novel framework for multi-step water level predicting by spatial–temporal deep learning models based on integrated physical models S. Zhang et al.
- Spatio-Temporal Graph Neural Networks for Streamflow Prediction in the Upper Colorado Basin A. Akkala et al.
- Multi-step ahead probabilistic forecasting of multiple hydrological variables for multiple stations Z. Zhang et al.
- Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al.
- An explainable physics-aware deep learning framework with improved spatiotemporal dependence matrices and signal decomposition for multi-station uncertainty daily runoff simulation W. Wu et al.
- River reach-level machine learning estimation of nutrient concentrations in Great Britain C. Tso et al.
- Graph neural network method for the intelligent selection of river system D. Wang & H. Qian
- Predicting the urban stormwater drainage system state using the Graph-WaveNet M. Li et al.
- Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources J. Willard et al.
- Alternative Hydraulic Modeling Method Based on Recurrent Neural Networks: From HEC-RAS to AI A. Rugină
- Hybrid bio-inspired optimization with artificial neural networks for efficient flood routing in watershed management N. Elshaboury et al.
- Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty
- Precipitation prediction over the upper Indus Basin from large-scale circulation patterns using Gaussian processes K. Tazi et al.
Saved (final revised paper)
Latest update: 16 May 2026
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...
