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.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! As airline passengers slog through a summer of rampant flight delays and cancellations, airlines are grappling with a massive post-pandemic increase in air travel demand and a long-term pilot shortage – while also prioritizing safety. Aerospace leader Airbus is betting autonomous and AI-driven commercial flight functions can bridge that gap. Wayfinder, a research project within Acubed, the Silicon Valley innovation center of Airbus, is developing autonomous flight and machine learning solutions for the next generation of aircraft. Its core mission is to build a "scalable, certifiable autonomy system capable of powering a range of self-piloted aircraft applications in single pilot operations."

An automatic pilot has landed a plane using image-recognition artificial intelligence to locate the runway. At large airports, systems on the ground beam up the position of the runway to guide automatic systems. But in late May a new AI tool landed a small plane carrying passengers, by "sight" alone at Austria's Diamond Aircraft airfield. One expert said it could potentially improve flight safety. The new system, developed by researchers at the technical universities of Braunschweig and Munich, processes visual data of the runway and then adjusts the plane's flight controls, without human assistance.