Please use this identifier to cite or link to this item:
Convection indicator for pre-tactical air traffic flow management using neural networks
Full metadata record
DC FieldValueLanguage
dc.contributor.authorJardines, Anieles_ES
dc.contributor.authorSoler, Manueles_ES
dc.contributor.authorCervantes, Alejandroes_ES
dc.contributor.authorGarcía-Heras, Javieres_ES
dc.contributor.authorSimarro Grande, Juan Pabloes_ES
dc.identifier.citationMachine Learning with Applications. 2021, 5, 100053es_ES
dc.description.abstractConvective weather is a large source of disruption for air traffic management operations. Being able to predict thunderstorms the day before operations can help traffic managers plan around weather and improve air traffic flow management operations. In this paper, machine learning is applied on data from satellite storm observations and ensemble numerical weather prediction products to detect convective weather 36 h in advance. The learning task is formulated as a binary classification problem and a neural network is trained to predict the occurrence of storms. The neural network results are used to develop a probabilistic based convection indicator capable of outperforming existing convection indicators found in the literature. Lastly, applications of the neural network based indicator in an air traffic management setting are presented.es_ES
dc.description.sponsorshipfinancial support of the Spanish Ministry of Science, Innovation and Universities under grant RTI2018-098471-B-C32. The project has received funding from the SESAR Joint Undertaking within the framework of the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891965.es_ES
dc.rightsLicencia CC: Reconocimiento–NoComercial–SinObraDerivada CC BY-NC-NDes_ES
dc.subjectAir traffic managementes_ES
dc.subjectNumerical weather predictiones_ES
dc.subjectSatellite imageses_ES
dc.subjectMachine learninges_ES
dc.titleConvection indicator for pre-tactical air traffic flow management using neural networkses_ES
Appears in Collections:Artículos científicos 2019-2022

Files in This Item:
  File Description SizeFormat 
5,43 MBAdobe PDFThumbnail
Show simple item record

Items in Arcimis are protected by Creative Commons License, unless otherwise indicated.

Arcimis Repository
Nota Legal Contacto y sugerencias