mlsanityData to 7 Oct 2026

Accelerating Flood Warnings by 10 Hours: The Power of River Network Topology in AI-enhanced Flood Forecasting

arXiv 2410.05536 ↗First posted 7 Oct 2024cs.CV cs.AI cs.IRcited by 1

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Abstract (arXiv)

Climate change-driven floods demand advanced forecasting models, yet Graph Neural Networks (GNNs) underutilize river network topology due to tree-like structures causing over-squashing from high node resistance distances. This study identifies this limitation and introduces a reachability-based graph transformation to densify topological connections, reducing resistance distances. Empirical tests show transformed-GNNs outperform EA-LSTM in extreme flood prediction, achieving 24-h water level accuracy equivalent to EA-LSTM's 14-h forecasts - a 71% improvement in long-term predictive horizon. The dense graph retains flow dynamics across hierarchical river branches, enabling GNNs to capture distal node interactions critical for rare flood events. This topological innovation bridges the gap between river network structure and GNN modeling, offering a scalable framework for early warning systems.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 24 of its 72 references that are in the map. First posted on arXiv 7 Oct 2024.

Builds on

24 of 72 references.

References · 24

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  6. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  7. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  8. 2020Simple and Deep Graph Convolutional Networkscited by 857
  9. 2022Recipe for a General, Powerful, Scalable Graph Transformercited by 702
  10. 2020On the Bottleneck of Graph Neural Networks and its Practical Implicationscited by 624
  11. 2021Understanding over-squashing and bottlenecks on graphs via curvaturecited by 471
  12. 2022Representing Long-Range Context for Graph Neural Networks with Global Attentioncited by 193
12 more
  1. 2016Theory-guided Data Science: A New Paradigm for Scientific Discovery from Datacited by 169
  2. 2023On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topologycited by 163
  3. 2020Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systemscited by 160
  4. 2022FoSR: First-order spectral rewiring for addressing oversquashing in GNNscited by 104
  5. 2022Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvaturecited by 96
  6. 2023Understanding Oversquashing in GNNs through the Lens of Effective Resistancecited by 94
  7. 2019Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasetscited by 68
  8. 2017A trans-disciplinary review of deep learning research for water resources scientistscited by 47
  9. 2019Universal Graph Transformer Self-Attention Networkscited by 41
  10. 2021Evaluation of deep learning models for multi-step ahead time series predictioncited by 25
  11. 2024The Merit of River Network Topology for Neural Flood Forecastingcited by 12
  12. 2021Flow aware Forwarding in SDN Datacenters Using a Knapsack PSO Based Solutioncited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Topology enables learning-based hydrodynamic prediction of the global river systemcited by 0