Efficient airport operations depend on appropriate actions and reactions to current constraints. Local weather events and their impact on airport performance may have network-wide effects. The classification of expected weather impacts enables efficient consideration in airport operations on a tactical level. We classify airport performance with recurrent and convolutional neural networks considering weather data. We are using London–Gatwick Airport to apply our developed approach. The weather data is derived from local meteorological reports and airport performance is derived from both flight plan data and reported delays. We show that the application of machine learning approaches is an appropriate method to quantify the correlation between decreased airport performance and the severity of local weather events. The developed models could achieve prediction accuracy higher than 90% for departure movements. We see our approach as one key element for a deeper understanding of interdependencies between local and network operations in the air transportation system.

高效的机场运营取决于对当前限制的适当行动和反应。当地天气事件及其对机场性能的影响可能会产生网络范围的影响。预期天气影响的分类能够在战术层面有效考虑机场运营。我们使用考虑天气数据的循环和卷积神经网络对机场性能进行分类。我们正在使用伦敦-盖特威克机场来应用我们开发的方法。天气数据来自当地气象报告，机场性能来自飞行计划数据和报告的延误。我们表明，机器学习方法的应用是量化机场性能下降与当地天气事件严重程度之间相关性的合适方法。所开发的模型可以实现高于 90% 的离场运动预测精度。我们将我们的方法视为深入了解航空运输系统中本地和网络运营之间相互依存关系的一个关键要素。