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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.24146 |
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| _version_ | 1866911182471823360 |
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| author | Lo, Ethan Zachary Lo, Dan Chie-Tien |
| author_facet | Lo, Ethan Zachary Lo, Dan Chie-Tien |
| contents | Accurate cyclone forecasting is essential for minimizing loss of life, infrastructure damage, and economic disruption. Traditional numerical weather prediction models, though effective, are computationally intensive and prone to error due to the chaotic nature of atmospheric systems. This study proposes a machine learning (ML) approach to forecasting tropical cyclone trajectory and status using time series data from the National Hurricane Center, including recently added best track wind radii. A two-stage ML pipeline is developed: a regression model first predicts cyclone features maximum wind speed, minimum pressure, trajectory length, and directional change using a sliding window of historical data. These outputs are then input into classification models to predict the cyclone's categorical status. Gradient boosting regression and three classifiers random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP) are evaluated. After hyperparameter tuning and synthetic minority oversampling (SMOTE), the RF classifier achieves the highest performance with 93% accuracy, outperforming SVM and MLP across precision, recall, and F1 score. The RF model is particularly robust in identifying minority cyclone statuses and minimizing false negatives. Regression results yield low mean absolute errors, with pressure and wind predictions within about 2.2 mb and 2.4 kt, respectively. These findings demonstrate that ML models, especially ensemble-based classifiers, offer an effective, scalable alternative to traditional forecasting methods, with potential for real-time cyclone prediction and integration into decision support systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24146 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluation of Machine and Deep Learning Techniques for Cyclone Trajectory Regression and Status Classification by Time Series Data Lo, Ethan Zachary Lo, Dan Chie-Tien Machine Learning Accurate cyclone forecasting is essential for minimizing loss of life, infrastructure damage, and economic disruption. Traditional numerical weather prediction models, though effective, are computationally intensive and prone to error due to the chaotic nature of atmospheric systems. This study proposes a machine learning (ML) approach to forecasting tropical cyclone trajectory and status using time series data from the National Hurricane Center, including recently added best track wind radii. A two-stage ML pipeline is developed: a regression model first predicts cyclone features maximum wind speed, minimum pressure, trajectory length, and directional change using a sliding window of historical data. These outputs are then input into classification models to predict the cyclone's categorical status. Gradient boosting regression and three classifiers random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP) are evaluated. After hyperparameter tuning and synthetic minority oversampling (SMOTE), the RF classifier achieves the highest performance with 93% accuracy, outperforming SVM and MLP across precision, recall, and F1 score. The RF model is particularly robust in identifying minority cyclone statuses and minimizing false negatives. Regression results yield low mean absolute errors, with pressure and wind predictions within about 2.2 mb and 2.4 kt, respectively. These findings demonstrate that ML models, especially ensemble-based classifiers, offer an effective, scalable alternative to traditional forecasting methods, with potential for real-time cyclone prediction and integration into decision support systems. |
| title | Evaluation of Machine and Deep Learning Techniques for Cyclone Trajectory Regression and Status Classification by Time Series Data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.24146 |