# Storm Surge Modeling in the AI ERA: Using LSTM-based Machine Learning for Enhancing Forecasting Accuracy

arXiv: https://arxiv.org/abs/2403.04818 · HTML: https://mlsanity.com/paper/2403.04818/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 53 references that are in the map.
- First posted on arXiv 7 Mar 2024.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions](https://mlsanity.com/paper/2204.09501/) (2022)
- [A Framework for Flexible Peak Storm Surge Prediction](https://mlsanity.com/paper/2204.13168/) (2022)

## Built on by (most cited first, 6 of 6)

- [Advancing Spatio-temporal Storm Surge Prediction with Hierarchical Deep Neural Networks](https://mlsanity.com/paper/2410.12823/) (2024)
- [A Neural Operator Emulator for Coastal and Riverine Shallow Water Dynamics](https://mlsanity.com/paper/2502.14782/) (2025)
- [Storm Surge in Color: RGB-Encoded Physics-Aware Deep Learning for Storm Surge Forecasting](https://mlsanity.com/paper/2506.21743/) (2025)
- [Projecting U.S. coastal storm surge risks and impacts with deep learning](https://mlsanity.com/paper/2506.13963/) (2025)
- [A Comparative Study of Convolutional and Recurrent Neural Networks for Storm Surge Prediction in Tampa Bay](https://mlsanity.com/paper/2408.05797/) (2024)
- [HURRI-GAN: A Novel Approach for Hurricane Bias-Correction Beyond Gauge Stations using Generative Adversarial Networks](https://mlsanity.com/paper/2603.06649/) (2026)

## Abstract (arXiv)

Physics simulation results of natural processes usually do not fully capture the real world. This is caused for instance by limits in what physical processes are simulated and to what accuracy. In this work we propose and analyze the use of an LSTM-based deep learning network machine learning (ML) architecture for capturing and predicting the behavior of the systemic error for storm surge forecast models with respect to real-world water height observations from gauge stations during hurricane events. The overall goal of this work is to predict the systemic error of the physics model and use it to improve the accuracy of the simulation results post factum. We trained our proposed ML model on a dataset of 61 historical storms in the coastal regions of the U.S. and we tested its performance in bias correcting modeled water level data predictions from hurricane Ian (2022). We show that our model can consistently improve the forecasting accuracy for hurricane Ian -- unknown to the ML model -- at all gauge station coordinates used for the initial data. Moreover, by examining the impact of using different subsets of the initial training dataset, containing a number of relatively similar or different hurricanes in terms of hurricane track, we found that we can obtain similar quality of bias correction by only using a subset of six hurricanes. This is an important result that implies the possibility to apply a pre-trained ML model to real-time hurricane forecasting results with the goal of bias correcting and improving the produced simulation accuracy. The presented work is an important first step in creating a bias correction system for real-time storm surge forecasting applicable to the full simulation area. It also presents a highly transferable and operationally applicable methodology for improving the accuracy in a wide range of physics simulation scenarios beyond storm surge forecasting.