Floods can be very destructive causing heavy damage to life, property, and livelihoods. Global climate change and the consequent sea-level rise have increased the occurrence of extreme weather events, resulting in elevated and frequent flood risk. Therefore, accurate and timely flood forecasting in coastal river systems is critical to facilitate good flood management. However, the computational tools currently used are either slow or inaccurate. In this paper, we propose a Flood prediction tool using Graph Transformer Network (FloodGTN) for river systems. More specifically, FloodGTN learns the spatio-temporal dependencies of water levels at different monitoring stations using Graph Neural Networks (GNNs) and an LSTM. It is currently implemented to consider external covariates such as rainfall, tide, and the settings of hydraulic structures (e.g., outflows of dams, gates, pumps, etc.) along the river. We use a Transformer to learn the attention given to external covariates in computing water levels. We apply the FloodGTN tool to data from the South Florida Water Management District, which manages a coastal area prone to frequent storms and hurricanes. Experimental results show that FloodGTN outperforms the physics-based model (HEC-RAS) by achieving higher accuracy with 70% improvement while speeding up run times by at least 500x.

翻译：洪水具有极大破坏性，对生命、财产和生计造成严重损害。全球气候变化及其导致的海平面上升增加了极端天气事件的发生频率，导致洪水风险升高且愈加频繁。因此，沿海河流系统准确及时的洪水预报对于促进良好的洪水管理至关重要。然而，目前使用的计算工具要么速度较慢，要么精度不足。本文提出了一种基于图变换器网络的河流系统洪水预测工具FloodGTN。具体而言，FloodGTN利用图神经网络和LSTM学习不同监测站点水位数据的时空依赖关系。该工具当前实现中考虑了降雨、潮汐以及河流沿线水工结构设置（如大坝出流、闸门、水泵等）等外部协变量。我们采用变换器来学习计算水位时对外部协变量的注意力权重。将FloodGTN工具应用于南佛罗里达水资源管理区的数据——该区域管理着频繁遭受风暴和飓风侵袭的沿海地区。实验结果表明，FloodGTN在精度上比基于物理模型的HEC-RAS提升了70%，同时运行速度至少加快500倍。