Improving the Typhoon Type Index by Integrating Strong Wind and Heavy Rainfall Using Machine Learning

In recent years, climate change has led to a clear increase in both the frequency and intensity of extreme weather events. Taiwan lies along major typhoon tracks in the western North Pacific, where typhoons represent one of the most significant natural hazards. The strong winds and heavy rainfall associated with typhoons frequently cause flooding, agricultural losses, and damage to critical infrastructure. In practice, however, the severity of typhoon-related disasters does not always correspond to traditional typhoon intensity classifications based primarily on central pressure and wind speed, indicating that wind-based classifications alone may not adequately represent actual disaster impacts.

This study utilizes hourly meteorological station observations to investigate the wind and rainfall characteristics of historical typhoon events in Taiwan. Multiple machine learning and regression models are applied, together with residual analysis, to quantify typhoon characteristics and construct a Typhoon Type Index (TTI). Based on the relative behavior of wind and rainfall during individual events, different typhoon types are further examined to identify their occurrence patterns and characteristic differences across historical cases.

The results indicate that the TTI derived from machine learning–based classification models can effectively improve upon previous TTI formulations established using regression models alone. Moreover, typhoons with different wind–rainfall characteristics are associated with distinct patterns of disaster impacts, and in some cases, rainfall intensity better reflects disaster severity than wind speed. By offering an alternative perspective to conventional intensity-based classifications, this study contributes to improved typhoon disaster risk assessment and provides useful insights for future disaster mitigation and preparedness strategies.