Title:
Machine Learning Enabled Turbulence Prediction Using Flight Data for Safety Analysis

dc.contributor.author Emara, Mariam
dc.contributor.author dos Santos, Marcos
dc.contributor.author Chartier, Noah
dc.contributor.author Ackley, Jamey
dc.contributor.author Puranik, Tejas G.
dc.contributor.author Payan, Alexia P.
dc.contributor.author Kirby, Michelle R.
dc.contributor.author Pinon, Olivia J.
dc.contributor.author Mavris, Dimitri N.
dc.contributor.corporatename Georgia Institute of Technology. Aerospace Systems Design Laboratory en_US
dc.contributor.corporatename International Council of the Aeronautical Sciences
dc.date.accessioned 2023-02-13T17:08:52Z
dc.date.available 2023-02-13T17:08:52Z
dc.date.issued 2021-09
dc.description Presented at the 32nd ICAS Congress, Shanghai, China (2022). en_US
dc.description.abstract The hazards posed by turbulence remain an important issue in commercial aviation safety analysis. Turbulence is among the leading cause of in-flight injury to passengers and flight attendants. Current methods of turbulence detection may suffer from sparse or inaccurate forecast data sets, low spatial and temporal resolution , and lack of in-situ reports. The increased availability of flight data records offers an opportunity to improve the state-of-the-art in turbulence detection. The Eddy Dissipation Rate (EDR) is consistently recognized as a reliable measure of turbulence and is widely used in the aviation industry. In this paper, both classification and regression supervised machine learning models are used in conjunction with flight operations quality assurance (FOQA) data collected from 6,000 routine flights to estimate the EDR (and thereby turbulence severity) in future time horizons. Data from routine airline operations that encountered different levels of turbulence is collected and analyzed for this purpose. Results indicate that the models are able to perform reasonably well in predicting the EDR and turbulence severity around 10 seconds prior to encountering a turbulence event. Continuous deployment of the model enables obtaining a near-continuous prediction of possible future turbulence events and builds the capability towards an early warning system for pilots and flight attendants. en_US
dc.identifier.citation Emara, M; et al. (2021) "Machine Learning Enabled Turbulence Prediction Using Flight Data for Safety Analysis". 32nd ICAS Congress Proceedings. 32nd ICAS Congress. en_US
dc.identifier.uri http://hdl.handle.net/1853/70283
dc.language.iso en_US en_US
dc.publisher International Council of the Aeronautical Sciences (ICAS) en_US
dc.publisher Georgia Institute of Technology
dc.publisher.original International Council of the Aeronautical Sciences (ICAS)
dc.subject Machine learning en_US
dc.subject Turbulence prediction en_US
dc.subject Safety en_US
dc.title Machine Learning Enabled Turbulence Prediction Using Flight Data for Safety Analysis en_US
dc.type Text
dc.type.genre Paper
dspace.entity.type Publication
local.contributor.author Payan, Alexia P.
local.contributor.author Mavris, Dimitri N.
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
local.contributor.corporatename Aerospace Systems Design Laboratory (ASDL)
local.contributor.corporatename College of Engineering
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relation.isOrgUnitOfPublication a8736075-ffb0-4c28-aa40-2160181ead8c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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