用彩色图像表示海水高度，提升风暴潮预测精度与泛化能力
Storm Surge in Color: RGB-Encoded Physics-Aware Deep Learning for Storm Surge Forecasting
- 将无结构水位数据转为RGB图像，适配深度学习模型
- 结合风场和地形数据，实现48小时多区域精准预测
- 适合气象、海洋与防灾领域研究者使用
风暴潮预测对沿海灾害应对至关重要，但现有机器学习方法普遍存在空间分辨率低、依赖岸站数据、泛化能力差的问题。许多模型直接处理非结构化空间数据，难以适配现代深度学习架构。本文提出一种新方法：将无结构水位场投影到结构化的红绿蓝（RGB）编码图像表示中，从而应用卷积长短期记忆网络（ConvLSTM）进行端到端时空预测。模型引入真实风场作为动态条件信号，以及地形-海深数据作为静态输入，捕捉潮汐演化的物理驱动因素。在墨西哥湾大规模合成风暴数据集上评估，该方法在德克萨斯州多个区域均表现出稳健的48小时预测性能，并具备良好的空间可扩展性，适用于其他沿海地区。通过结构化表示、物理驱动输入与可扩展深度学习的结合，本研究显著提升了风暴潮预测的实用性、适应性与可解释性。
原文摘要 · Abstract (English)
Storm surge forecasting plays a crucial role in coastal disaster preparedness, yet existing machine learning approaches often suffer from limited spatial resolution, reliance on coastal station data, and poor generalization. Moreover, many prior models operate directly on unstructured spatial data, making them incompatible with modern deep learning architectures. In this work, we introduce a novel approach that projects unstructured water elevation fields onto structured Red Green Blue (RGB)-encoded image representations, enabling the application of Convolutional Long Short Term Memory (ConvLSTM) networks for end-to-end spatiotemporal surge forecasting. Our model further integrates ground-truth wind fields as dynamic conditioning signals and topo-bathymetry as a static input, capturing physically meaningful drivers of surge evolution. Evaluated on a large-scale dataset of synthetic storms in the Gulf of Mexico, our method demonstrates robust 48-hour forecasting performance across multiple regions along the Texas coast and exhibits strong spatial extensibility to other coastal areas. By combining structured representation, physically grounded forcings, and scalable deep learning, this study advances the frontier of storm surge forecasting in usability, adaptability, and interpretability.
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