Prediction of aircraft delay at busy airport considering weather information with machine learning techniques

In this study, we predict aircraft cancelations and delays applying numerical model forecast weather data and contribute to improving the efficiency and safety of the aviation industry. Recently, due to climate change, extreme weather events have been occurring frequently around the world, which has led to an increase in flight cancellations and delays. To address these issues, this study developed a prediction model using a machine learning algorithm.The model developed in this study can be applied to the real-world aviation industry to benefit both air traffic control systems and airlines. For air traffic control, predictive air traffic management will enable efficient and reliable operations, and for airlines, more accurate flight cancellation and delay prediction will enable efficient operations such as flight management and crew assignment. In addition, aircraft safety can be improved by minimizing risk factors due to weather.