# Predicting Hurricane Trajectories using a Recurrent Neural Network

arXiv: https://arxiv.org/abs/1802.02548 · HTML: https://mlsanity.com/paper/1802.02548/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2018.

## Built on by (most cited first, 18 of 18)

- [An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions](https://mlsanity.com/paper/2204.09501/) (2022)
- [Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities](https://mlsanity.com/paper/2307.10803/) (2023)
- [Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data](https://mlsanity.com/paper/2401.15726/) (2024)
- [Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data](https://mlsanity.com/paper/2412.10450/) (2024)
- [Predicting Landfall's Location and Time of a Tropical Cyclone Using Reanalysis Data](https://mlsanity.com/paper/2103.16108/) (2021)
- [Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast](https://mlsanity.com/paper/2202.13336/) (2022)
- [Prediction of Landfall Intensity, Location, and Time of a Tropical Cyclone](https://mlsanity.com/paper/2103.16180/) (2021)
- [Learning future terrorist targets through temporal meta-graphs](https://mlsanity.com/paper/2104.10398/) (2021)
- [Forecasting formation of a Tropical Cyclone Using Reanalysis Data](https://mlsanity.com/paper/2212.06149/) (2022)
- [Ridge Regression on Riemannian Manifolds for Time-Series Prediction](https://mlsanity.com/paper/2411.18339/) (2024)
- [Unavailable Transit Feed Specification: Making it Available with Recurrent Neural Networks](https://mlsanity.com/paper/2102.10323/) (2021)
- [Data-Based Models for Hurricane Evolution Prediction: A Deep Learning Approach](https://mlsanity.com/paper/2111.12683/) (2021)
- [GraphTransformers for Geospatial Forecasting of Hurricane Trajectories](https://mlsanity.com/paper/2310.20174/) (2023)
- [Enhancing Tropical Cyclone Path Forecasting with an Improved Transformer Network](https://mlsanity.com/paper/2505.00495/) (2025)
- [Evaluation of Machine and Deep Learning Techniques for Cyclone Trajectory Regression and Status Classification by Time Series Data](https://mlsanity.com/paper/2509.24146/) (2025)
- [Phys-Diff: A Physics-Inspired Latent Diffusion Model for Tropical Cyclone Forecasting](https://mlsanity.com/paper/2603.00521/) (2026)
- [Improving Ensemble Forecasts of Abnormally Deflecting Tropical Cyclones with Fused Atmosphere-Ocean-Terrain Data](https://mlsanity.com/paper/2603.29200/) (2026)
- [Tianmu-TC: Physics-constraints Generative Artificial Intelligence for Global Tropical Cyclone Forecasting](https://mlsanity.com/paper/2608.18500/) (2026)

## Abstract (arXiv)

Hurricanes are cyclones circulating about a defined center whose closed wind speeds exceed 75 mph originating over tropical and subtropical waters. At landfall, hurricanes can result in severe disasters. The accuracy of predicting their trajectory paths is critical to reduce economic loss and save human lives. Given the complexity and nonlinearity of weather data, a recurrent neural network (RNN) could be beneficial in modeling hurricane behavior. We propose the application of a fully connected RNN to predict the trajectory of hurricanes. We employed the RNN over a fine grid to reduce typical truncation errors. We utilized their latitude, longitude, wind speed, and pressure publicly provided by the National Hurricane Center (NHC) to predict the trajectory of a hurricane at 6-hour intervals. Results show that this proposed technique is competitive to methods currently employed by the NHC and can predict up to approximately 120 hours of hurricane path.