mlsanityData to 7 Oct 2026

Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks

arXiv 2107.07039 ↗First posted 7 Jul 2021cs.LG eess.SPcited by 12

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Abstract (arXiv)

The frequency and impact of floods are expected to increase due to climate change. It is crucial to predict streamflow, consequently flooding, in order to prepare and mitigate its consequences in terms of property damage and fatalities. This paper presents a Graph Convolutional GRUs based model to predict the next 36 hours of streamflow for a sensor location using the upstream river network. As shown in experiment results, the model presented in this study provides better performance than the persistence baseline and a Convolutional Bidirectional GRU network for the selected study area in short-term streamflow prediction.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 9 of its 26 references that are in the map. First posted on arXiv 7 Jul 2021.

Builds on

9 of 26 references.

References · 9

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  2. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  3. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  4. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  5. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 525
  6. 2016Structured Sequence Modeling with Graph Convolutional Recurrent Networkscited by 273
  7. 2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Modelscited by 118
  8. 2020A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resourcescited by 30
  9. 2019Decentralized Flood Forecasting Using Deep Neural Networkscited by 4

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Cited by year

Citing papers per year: 2021: 2; 2022: 0; 2023: 6; 2024: 3; 2025: 1.

12 papers.

  1. 2023Performance of ChatGPT on the US Fundamentals of Engineering Exam: Comprehensive Assessment of Proficiency and Potential Implications for Professional Environmental Engineering Practicecited by 15
  2. 2023Platform-Independent and Curriculum-Oriented Intelligent Assistant for Higher Educationcited by 12
  3. 2023Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrologycited by 11
  4. 2023Graph Neural Network for spatiotemporal data: methods and applicationscited by 6
  5. 2023Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resourcescited by 6
  6. 2021DEM Super-Resolution with EfficientNetV2cited by 5
  7. 2023EfficientTempNet: Temporal Super-Resolution of Radar Rainfallcited by 3
  8. 2024Beyond Grid Data: Exploring Graph Neural Networks for Earth Observationcited by 3
  9. 2021TempNet -- Temporal Super Resolution of Radar Rainfall Products with Residual CNNscited by 2
  10. 2024Towards Generalized Hydrological Forecasting using Transformer Models for 120-Hour Streamflow Predictioncited by 1
  11. 2025HydroGAT: Distributed Heterogeneous Graph Attention Transformer for Spatiotemporal Flood Predictioncited by 1
  12. 2024Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approachcited by 0