Machine Learning-Based Prediction of Tropical Cyclone Intensification Over the North Indian Ocean Using ERA5 Reanalysis

Machine Learning models are rapidly becoming popular for complementing, enhancing, and in some cases, replacing traditional numerical models. This study presents a data-driven framework for predicting 24-hour tropical cyclone intensification over the North Indian Ocean using supervised machine learning and ERA5 reanalysis data. Cyclones that formed over Bay of Bengal and the Arabian Sea during the period 1990–2024 are considered here.  We have integrated environmental parameters from ERA5 with intensity records from the IBTrACS archive, excluding early developmental stages and retaining only dynamically mature systems. Intensification is formulated as a binary classification problem based on the sign of the 24-hour change in maximum sustained wind speed. While this captures general strengthening behaviour, it does not distinguish between moderate and rapid intensification, nor does it estimate the magnitude of intensity change. Five machine learning models—Logistic Regression, Random Forest, Extra Trees, Support Vector Machine, and Multilayer Perceptron—are trained and evaluated. Results indicate that the Random Forest classifier has achieved the highest accuracy. Feature-importance analysis reveals strong physical consistency, highlighting the dominant roles of upper-level circulation, sea surface temperature, vertical wind shear, and atmospheric moisture in regulating short-term intensification. Cyclone Montha (2025) is used as a test case to illustrate the model's real-world applicability and is validated outside of historical data. The model-predicted intensification probability is estimated as 0.943, which indicates good performance. Although a single case study does not constitute statistical validation, this illustrates the applicability of data-driven models in tropical cyclone intensity estimation. The results encourage further investigations into the use of such data-driven models in tropical cyclone intensity prediction, which aids disaster management efforts.