DocumentCode
2569450
Title
Estimate weather impacted airport capacity using ensemble learning
Author
Wang, Yao
Author_Institution
NASA Ames Research Center
fYear
2011
fDate
16-20 Oct. 2011
Firstpage
1
Lastpage
13
Abstract
Ensemble BDT consistently outperforms the single SVM classifier. This demonstrates that multiple classifier systems are more robust in the presence of noise and other imperfections in data as compared to a single classifier system. • The BDT classifier provides very good estimates of the runway configuration using the airport weather. • The AAR classification predictions by BDT for 2 and 4 hour look-ahead times are excellent. For 6-hour AAR prediction, the performance of the BDT classifier is not bad, AUC is 86% for EWR and 92% for ORD. • The AAR prediction results using BDT models for EWR are not as good as for ORD (Weather factors).
fLanguage
English
Publisher
ieee
Conference_Titel
Digital Avionics Systems Conference (DASC), 2011 IEEE/AIAA 30th
Conference_Location
Seattle, WA, USA
ISSN
2155-7195
Print_ISBN
978-1-61284-797-9
Type
conf
DOI
10.1109/DASC.2011.6096196
Filename
6096196
Link To Document