通过优化河网拓扑结构，让洪水预测提前10小时预警。
Accelerating Flood Warnings by 10 Hours: The Power of River Network Topology in AI-enhanced Flood Forecasting
- 用可达性转换增强河网连接，降低节点间阻力距离。
- 模型预测精度相当于传统方法提前71%时间，最长可提前三天。
- 适合需要早期预警的水利系统与气候变化应对研究。
气候变化引发的洪水亟需先进预报模型，但图神经网络（GNN）因河网树状结构导致高阻力距离，难以有效利用拓扑信息。本研究提出基于可达性的图变换方法，增强拓扑连接并降低阻力距离。实验证明，经过转换的GNN在极端洪水预测中优于EA-LSTM模型，24小时水位预测精度相当于EA-LSTM的14小时结果，长期预测能力提升71%。该方法保留了多级河段间的流动动态，使GNN能够捕捉远距离节点间的关联，对罕见洪水事件至关重要。这一拓扑创新弥合了河网结构与GNN建模间的鸿沟，为早期预警系统提供可扩展框架。
原文摘要 · Abstract (English)
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.
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