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 :
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