Intelligent Algorithm for Aviation Flight Prediction and Dynamic Flight Adjustment

With the rapid development of the aviation industry, flight prediction and dynamic adjustment are crucial in aviation operation management. Flight delay is one of the main problems faced by airlines and passengers. Flight delays are caused by a combination of multiple factors, which require comprehensive consideration and analysis to develop effective measures to reduce the occurrence of flight delays. To address this issue, machine learning algorithms can be used to predict flight delays. This article utilizes a gradient boosting framework and combines multi-dimensional data such as historical flight data, weather information, and airport operations to design a flight delay prediction model. This model can accurately predict the possibility of flight delays through training and learning, providing strong support for airlines to formulate response strategies in advance. The experimental results showed that the continuously optimized prediction unit system in this article reduced the average absolute error by 2.54 minutes and the average root mean square error by 1.64 minutes under all windows. It can be shown that the prediction model in this paper has high accuracy in predicting flight dynamics.