Aviation Turbulence Forecasting at Upper Levels with Machine Learning Techniques Based on Regression...
Aviation Turbulence Forecasting at Upper Levels with Machine Learning Techniques Based on Regression Trees
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Publisher
Boston: American Meteorological Society
Journal title
Language
English
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Publisher
Boston: American Meteorological Society
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Scope and Contents
Contents
We explore the use of machine learning (ML) techniques, namely, regression trees (RT), for the purpose of aviation turbulence forecasting at upper levels [20–45 kft (∼6–14 km) in altitude]. In particular, we develop a series of RT-based algorithms that include random forests (RF) and gradient-boosted regression trees (GBRT) methods. Numerical weath...
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Full title
Aviation Turbulence Forecasting at Upper Levels with Machine Learning Techniques Based on Regression Trees
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TN_cdi_proquest_journals_2511144374
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2511144374
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ISSN
1558-8424
E-ISSN
1558-8432
DOI
10.1175/JAMC-D-20-0116.1
