# Storm Surge in Color: RGB-Encoded Physics-Aware Deep Learning for Storm Surge Forecasting

arXiv: https://arxiv.org/abs/2506.21743 · HTML: https://mlsanity.com/paper/2506.21743/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Deep multi-scale video prediction beyond mean square error](https://mlsanity.com/paper/1511.05440/) (2015)
- [A Framework for Flexible Peak Storm Surge Prediction](https://mlsanity.com/paper/2204.13168/) (2022)
- [Storm Surge Modeling in the AI ERA: Using LSTM-based Machine Learning for Enhancing Forecasting Accuracy](https://mlsanity.com/paper/2403.04818/) (2024)
- [A Discontinuous Galerkin Finite Element Model for Compound Flood Simulations](https://mlsanity.com/paper/2307.14302/) (2023)
- [Advancing Spatio-temporal Storm Surge Prediction with Hierarchical Deep Neural Networks](https://mlsanity.com/paper/2410.12823/) (2024)

## Built on by (most cited first, 2 of 2)

- [StormNet: Improving storm surge predictions with a GNN-based spatio-temporal offset forecasting model](https://mlsanity.com/paper/2604.20688/) (2026)
- [A Model-Agnostic Physics-Guided Adapter for Few-Shot Transfer of Coastal Flood Prediction Models to Unseen Regions](https://mlsanity.com/paper/2609.37565/) (2026)

## Abstract (arXiv)

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.