  With the continuous expansion of the scale of air transport, the demand for
aviation meteorological support also continues to grow. The impact of hazardous
weather on flight safety is critical. How to effectively use meteorological
data to improve the early warning capability of flight dangerous weather and
ensure the safe flight of aircraft is the primary task of aviation
meteorological services. In this work, support vector machine (SVM) models are
used to predict hazardous flight weather, especially for meteorological
conditions with high uncertainty such as storms and turbulence. SVM is a
supervised learning method that distinguishes between different classes of data
by finding optimal decision boundaries in a high-dimensional space. In order to
meet the needs of this study, we chose the radial basis function (RBF) as the
kernel function, which helps to deal with nonlinear problems and enables the
model to better capture complex meteorological data structures. During the
model training phase, we used historical meteorological observations from
multiple weather stations, including temperature, humidity, wind speed, wind
direction, and other meteorological indicators closely related to flight
safety. From this data, the SVM model learns how to distinguish between normal
and dangerous flight weather conditions.
