Storm Surge in Color: RGB-Encoded Physics-Aware Deep Learning for Storm Surge Forecasting
Journal:
arXiv
Published Date:
Jun 26, 2025
Abstract
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.
