Machine Learning for Advancing Risk Precursor Identification Tools in Commercial Airline Terminal Area Operations (out of D318)
Description
This work uses a variety of machine learning techniques to recommend commercial terminal area aviation failure modes in addition to unstable vertical approach, evaluate these for machine learning feasibility, recommend the best failure mode and machine learning algorithm pairings, and incorporate these into the aviation risk precursor detection prototype back-end and user interface.
Detailed example
recommendations
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
This work uses a variety of machine learning techniques to recommend commercial terminal area aviation failure modes in addition to unstable vertical approach, evaluate these for machine learning feasibility, recommend the best failure mode and machine learning algorithm pairings, and incorporate these into the aviation risk precursor detection prototype back-end and user interface.
Controls / human review
ATO: Not reported; PIA: Not published