The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN)

dc.contributor.authorGuessoum, Sonia
dc.contributor.authorBelda, Santiago
dc.contributor.authorFerrandiz , Jose M.
dc.contributor.authorModiri, Sadegh
dc.contributor.authorRaut, Shrishail
dc.contributor.authorDhar, Sujata
dc.contributor.authorHeinkelmann , Robert
dc.contributor.authorSchuh , Harald
dc.date.accessioned2023-02-01T15:43:09Z
dc.date.available2023-02-01T15:43:09Z
dc.date.issued2022-11-29
dc.description.abstractAccurate Earth orientation parameter (EOP) predictions are needed for many applications, e.g., for the tracking and navigation of interplanetary spacecraft missions. One of the most difficult parameters to forecast is the length of day (LOD), which represents the variation in the Earth’s rotation rate since it is primarily affected by the torques associated with changes in atmospheric circulation. In this study, a new-generation time-series prediction algorithm is developed. The one-dimensional convolutional neural network (1D CNN), which is one of the deep learning methods, is introduced to model and predict the LOD using the IERS EOP 14 C04 and axial Z component of the atmospheric angular momentum (AAM), which was taken from the German Research Centre for Geosciences (GFZ) since it is strongly correlated with the LOD changes. The prediction procedure operates as follows: first, we detrend the LOD and Z-component series using the LS method, then, we obtain the residual series of each one to be used in the 1D CNN prediction algorithm. Finally, we analyze the results before and after introducing the AAM function. The results prove the potential of the proposed method as an optimal algorithm to successfully reconstruct and predict the LOD for up to 7 days.en
dc.identifier.eissn1424-8220
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/18132
dc.identifier.urihttps://doi.org/10.14279/depositonce-16925
dc.language.isoen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc550 Geowissenschaftende
dc.subject.otherone-dimensional convolutional neural networksen
dc.subject.other1D CNNen
dc.subject.otherlength of dayen
dc.subject.otheratmospheric angular momentumen
dc.subject.otherAAM functionen
dc.subject.otherpredictionen
dc.titleThe Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN)en
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.articlenumber9517
dcterms.bibliographicCitation.doi10.3390/s22239517
dcterms.bibliographicCitation.issue23
dcterms.bibliographicCitation.journaltitleSensors
dcterms.bibliographicCitation.originalpublishernameMDPI
dcterms.bibliographicCitation.originalpublisherplaceBasel
dcterms.bibliographicCitation.volume22
dcterms.rightsHolder.referenceCreative-Commons-Lizenz
tub.accessrights.dnbfree
tub.affiliationFak. 6 Planen Bauen Umwelt::Inst. Geodäsie und Geoinformationstechnik::FG Satellitengeodäsie
tub.publisher.universityorinstitutionTechnische Universität Berlin

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