Development and optimization of Machine Learning methods to predict weather-induced operating restrictions in air traffic management

Abstract

The Met4Airports project aims to apply methods of machine learning (ML) and artificial intelligence (AI) in order to provide predictions of the weather impact on planning and control parameters relevant for air traffic management (ATM), such as values of single and average flight delays as well as capacity values of runways and en-route airspace sectors. For this purpose, air traffic data such as pre-scheduled flight lists and up-to-date time stamps from the A-CDM system (Airport Collaborative Decision Making) are combined with meteorological forecast data from nowcasting and numerical weather prediction models, with the ML models being trained on corresponding historical data sets. While different approaches for modeling of the air traffic system regarding temporal discretization and sampling were investigated and extensive feature engineering and feature importance studies were conducted, a major challenge was the selection and optimization of appropriate ML model architectures to process the 2-dimensional data input from NowCastMix-Aviation (NCM-A) and the ICON-D2 model. Within this scope, it was found that comparatively simple multilayer perceptrons (MLPs), for whose data input the 2D NCM/ICON arrays are pooled in advance, show a better performance for all use cases than convolutional neural networks (CNNs), which are a staple of modern image processing. Meteorological feature studies have been performed, aimed at determining the size of the relevant area around the airports, the required spatial resolution of the weather forecasting data and the relevant meteorological model prediction parameters. Additionally, also systematic hyperparameter studies were performed on all considered ML models. Ultimately, the developed preliminary prototypes not only have shown to provide viable impact predictions on a set of thunderstorm days which were selected for historical process testing, but apparently also yield advantages over the already available information from the A-CDM system, which constitute a comparative baseline. A detailed insight on the validation and testing of the ML model predictions is given by Knigge et al. [1].  Furthermore, it was found that the temporal and spatial accuracy of the weather forecast provided by the ICON-D2 model at lead times of up to 24 hours is generally good enough for the impact prediction on ATM target parameters. The project is funded by the German Federal Ministry for Digital and Transport (BMDV), coordinated by Deutscher Wetterdienst (DWD) and developed in close cooperation with the project partners ask – Innovative Visualisierungslösungen GmbH, Deutsche Flugsicherung (DFS), Fraport AG and Flughafen München GmbH (FMG).  [1] Knigge et al. Testing and validation of forecasts for weather-induced operating restrictions in air traffic management based on Machine Learning models, OSA2.4 Reducing weather risks to transport: air, sea and land, submitted for EMS 2023.