Storm track and intensity forecasting using a hybrid machine learning approach

Abstract

Storms, including cyclones, hurricanes, and typhoons, pose significant threats due to their destructive winds and heavy rainfall, causing extensive damage to infrastructure, ecosystems, and human lives. Accurate prediction of storm paths and intensities is crucial for disaster preparedness and minimizing losses. Despite advances in meteorological science and computational modeling, accurately forecasting sudden changes in storm behavior, such as rapid intensification or unexpected weakening, remains challenging. Traditional numerical weather prediction (NWP) models integrate physical laws governing atmospheric conditions but struggle with uncertainties stemming from rapid weather variations, incomplete observations, and computational constraints. This study proposes a hybrid approach integrating sequence-based deep learning (Long Short-Term Memory (LSTM) and Transformers) with gradient boosting techniques (XGBoost, LightGBM) for predicting storm tracks and intensities. Additionally, physics-informed constraints such as storm kinetic energy, Coriolis force, and land interaction effects are incorporated to enhance the physical consistency and interpretability of the models. By combining machine learning models with fundamental meteorological principles, this work aims to improve the reliability and accuracy of cyclone forecasts. The main contributions of this study include: (1) the development of a multi-modal hybrid model combining deep learning and tree-based machine learning methods; (2) incorporation of physics-informed constraints to enhance model generalization and physical validity; and (3) rigorous evaluation of model performance using real-world cyclone datasets with robust metrics, including the Haversine distance for path prediction and Mean Absolute Error (MAE) for intensity estimation.