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Prediction of Weather Impacted Airport Capacity using Ensemble LearningEnsemble learning with the Bagging Decision Tree (BDT) model was used to assess the impact of weather on airport capacities at selected high-demand airports in the United States. The ensemble bagging decision tree models were developed and validated using the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) data and weather forecast at these airports. The study examines the performance of BDT, along with traditional single Support Vector Machines (SVM), for airport runway configuration selection and airport arrival rates (AAR) prediction during weather impacts. Testing of these models was accomplished using observed weather, weather forecast, and airport operation information at the chosen airports. The experimental results show that ensemble methods are more accurate than a single SVM classifier. The airport capacity ensemble method presented here can be used as a decision support model that supports air traffic flow management to meet the weather impacted airport capacity in order to reduce costs and increase safety.
Document ID
Document Type
Conference Paper
Wang, Yao Xun (NASA Ames Research Center Moffett Field, CA United States)
Date Acquired
June 16, 2014
Publication Date
October 16, 2011
Subject Category
Aeronautics (General)
Air Transportation and Safety
Meteorology and Climatology
Report/Patent Number
Meeting Information
Digital Avionica System Conference(Seattle, WA, USA)
Funding Number(s)
WBS: WBS 411931.
Distribution Limits
Public Use Permitted.
Weather impact
Airport capacity
Machine learning

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