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.


    Access

    Download


    Export, share and cite



    Title :

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


    Contributors:


    Publication date :

    2025




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown






    Improved airport operations planning by using tailored forecasts of severe weather

    Günther, Yves / Pick, Andreas / Kern, Stefan et al. | German Aerospace Center (DLR) | 2015

    Free access

    Lighting equipment for adverse weather operations

    Harding, W.T. | Engineering Index Backfile | 1945