Enhancing Tropical Cyclone Path Forecasting with an Improved Transformer Network
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912357049958400 |
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| author | Van Thanh, Nguyen Huynh, Nguyen Dang Tan, Nguyen Ngoc Minh, Nguyen Thai Hoang, Nguyen Nam |
| author_facet | Van Thanh, Nguyen Huynh, Nguyen Dang Tan, Nguyen Ngoc Minh, Nguyen Thai Hoang, Nguyen Nam |
| contents | A storm is a type of extreme weather. Therefore, forecasting the path of a storm is extremely important for protecting human life and property. However, storm forecasting is very challenging because storm trajectories frequently change. In this study, we propose an improved deep learning method using a Transformer network to predict the movement trajectory of a storm over the next 6 hours. The storm data used to train the model was obtained from the National Oceanic and Atmospheric Administration (NOAA) [1]. Simulation results show that the proposed method is more accurate than traditional methods. Moreover, the proposed method is faster and more cost-effective |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00495 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Tropical Cyclone Path Forecasting with an Improved Transformer Network Van Thanh, Nguyen Huynh, Nguyen Dang Tan, Nguyen Ngoc Minh, Nguyen Thai Hoang, Nguyen Nam Machine Learning Performance A storm is a type of extreme weather. Therefore, forecasting the path of a storm is extremely important for protecting human life and property. However, storm forecasting is very challenging because storm trajectories frequently change. In this study, we propose an improved deep learning method using a Transformer network to predict the movement trajectory of a storm over the next 6 hours. The storm data used to train the model was obtained from the National Oceanic and Atmospheric Administration (NOAA) [1]. Simulation results show that the proposed method is more accurate than traditional methods. Moreover, the proposed method is faster and more cost-effective |
| title | Enhancing Tropical Cyclone Path Forecasting with an Improved Transformer Network |
| topic | Machine Learning Performance |
| url | https://arxiv.org/abs/2505.00495 |