Tropical Cyclone Genesis Prediction Based on Support Vector Machine Considering Effects of Multiple Meteorological Parameters

Most coastal regions worldwide face great challenges from tropical cyclones (TCs). Accurate prediction of TC genesis is critical for conducting risk assessments and disaster mitigation. Compared to conventional statistical models, this study introduces a machine-learning-based approach utilizing a support vector machine (SVM) to predict TC genesis. Historical TC genesis data from the Japan Meteorological Agency (JMA), China Meteorological Administration (CMA), and Joint Typhoon Warning Center (JTWC) are utilized. Five meteorological parameters are extracted from climate reanalysis datasets: absolute vorticity, relative humidity, vertical velocity relative sea surface temperature, and vertical wind shear. A high-dimensional mapping between meteorological parameters and TC genesis labels is performed before classification training. The effects of proportions of positive/negative samples and training/testing sets on model performance are examined, aiming to identify the optimal solution. Different meteorological parameter combinations tested to achieve the highest classification accuracy. Temporal and spatial outcomes of TC predictions are compared with historical data to verify model accuracy. The proposed approach enables simultaneous determination of TC genesis locations and counts, significantly enhancing computational efficiency. It also uses meteorological parameters more comprehensively considering the underlying physical mechanisms. In future climate change scenarios, forecasted meteorological data can be directly incorporated to derive TC genesis patterns.