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Titel:

Forecasting Unstable Approaches with Boosting Frameworks and LSTM Networks​

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Martinez, Dario; Fernández, Antonio; Hernández, Pablo; Cristóbal, Samuel; Schwaiger, Florian; Nunez, José María; Ruiz, José Manuel
Abstract:
This paper presents a machine learning algorithm trained to predict unstable approach events. Predictive modeling for unstable approaches (UA) forecasting needs a precursors analysis to determine the most important indicators (features) of aircraft instability. However, since the definition of aircraft instability is entirely dependent on the airline, these precursors might change according to the applied criteria. Most of the times, these precursors are related to the operation, ATC inst...     »
Kongress- / Buchtitel:
SESAR Innovation Days​ 2019
Verlagsort:
Athens, Greece
Jahr:
2019
WWW:
https://www.sesarju.eu/node/3438
 BibTeX