• 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