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Title: Predictive classification and understanding of weather impact on airport performance through machine learning
Authors: Schultz, Michael
Reitmann, Stefan
Alam, Sameer
Keywords: Engineering::Aeronautical engineering
Issue Date: 2021
Source: Schultz, M., Reitmann, S. & Alam, S. (2021). Predictive classification and understanding of weather impact on airport performance through machine learning. Transportation Research Part C: Emerging Technologies, 131, 103119-.
Journal: Transportation Research Part C: Emerging Technologies 
Abstract: 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.
ISSN: 0968-090X
DOI: 10.1016/j.trc.2021.103119
Research Centres: Air Traffic Management Research Institute 
Rights: © 2021 Elsevier Ltd. All rights reserved. This paper was published in Transportation Research Part C: Emerging Technologies and is made available with permission of Elsevier Ltd.
Fulltext Permission: embargo_20231107
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Journal Articles

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