A Collection of Machine Learning Models for Improved Airport Operations Amidst Adverse Weather Conditions

In the face of escalating climate change, airports worldwide are finding themselves at the mercy of extreme weather events. This research paper presents a comprehensive system that models key indicators, aiding airport management during such challenging weather conditions. The system adopts an integrated approach, combining various machine learning models to provide a detailed projection of an airport's future state, drawing from past occurrences. The heart of the system is a model that focuses on the airport's peak service rate. This model meticulously correlates weather conditions and runway configurations with the 99th percentile of observed throughput from the training dataset. As such, the peak service rate model provides an estimate of the airport's capacity, which is essential for effective planning and resource allocation. Moreover, the system includes a predictive model that assesses the likelihood of air traffic flow management regulations based on weather data and calendar information. The robustness of this model against noise and uncertainty in the training dataset is fortified by the application of confident learning techniques and the inclusion of monotonic constraints. The system further enhances its capabilities by forecasting the potential entry rate of regulations, expressed in hourly arrivals, providing valuable insights that can guide proactive decision-making. By seamlessly integrating these three models, the system serves as an effective tool for airport operators and airlines. It enables operational optimisation and the development of strategic plans to mitigate the effects of increasing weather-related disruptions.