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The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN)
Accurate 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 ra...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740590/ https://www.ncbi.nlm.nih.gov/pubmed/36502228 http://dx.doi.org/10.3390/s22239517 |
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author | Guessoum, Sonia Belda, Santiago Ferrandiz, Jose M. Modiri, Sadegh Raut, Shrishail Dhar, Sujata Heinkelmann, Robert Schuh, Harald |
author_facet | Guessoum, Sonia Belda, Santiago Ferrandiz, Jose M. Modiri, Sadegh Raut, Shrishail Dhar, Sujata Heinkelmann, Robert Schuh, Harald |
author_sort | Guessoum, Sonia |
collection | PubMed |
description | Accurate 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. |
format | Online Article Text |
id | pubmed-9740590 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97405902022-12-11 The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN) Guessoum, Sonia Belda, Santiago Ferrandiz, Jose M. Modiri, Sadegh Raut, Shrishail Dhar, Sujata Heinkelmann, Robert Schuh, Harald Sensors (Basel) Article Accurate 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. MDPI 2022-12-06 /pmc/articles/PMC9740590/ /pubmed/36502228 http://dx.doi.org/10.3390/s22239517 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Guessoum, Sonia Belda, Santiago Ferrandiz, Jose M. Modiri, Sadegh Raut, Shrishail Dhar, Sujata Heinkelmann, Robert Schuh, Harald The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN) |
title | The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN) |
title_full | The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN) |
title_fullStr | The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN) |
title_full_unstemmed | The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN) |
title_short | The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN) |
title_sort | short-term prediction of length of day using 1d convolutional neural networks (1d cnn) |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740590/ https://www.ncbi.nlm.nih.gov/pubmed/36502228 http://dx.doi.org/10.3390/s22239517 |
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