Prediction of polar motion and UT1-UTC based on the hybrid EEMD_LSTM model
Accurate and real-time Earth rotation parameters (ERPs) are crucial for various scientific fields, including astronomy, geoscience, and oceanography. Although there are various space geodetic techniques, such as VLBI, GNSS, DORIS, and SLR, which can determine the ERPs with high accuracy, these metho...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-06-01
|
| Series: | Geo-spatial Information Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2025.2522154 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849707159814668288 |
|---|---|
| author | Chenxiang Wang Pengfei Zhang Wenbin Shen Jiayao Wang |
| author_facet | Chenxiang Wang Pengfei Zhang Wenbin Shen Jiayao Wang |
| author_sort | Chenxiang Wang |
| collection | DOAJ |
| description | Accurate and real-time Earth rotation parameters (ERPs) are crucial for various scientific fields, including astronomy, geoscience, and oceanography. Although there are various space geodetic techniques, such as VLBI, GNSS, DORIS, and SLR, which can determine the ERPs with high accuracy, these methods have a certain delay and are difficult to meet the needs of real-time applications. Therefore, the ERPs prediction with high accuracy is very significant. Conventional approaches to predicting ERPs are mainly based on the linear models. However, it is not always optimal for the non-linear effects of ERPs. In this paper, we propose a model that combines Ensemble Empirical Mode Decomposition (EEMD) with Long Short-Term Memory (LSTM), denoted as EEMD_LSTM. We make the prediction experiments by using the EEMD_LSTM model, all experiments use the IERS EOP 20C04 as the basic series, the sliding window set as one week, and the prediction accuracies of polar motion (PM) and UT1-UTC from 2011 to 2021 are analyzed. Then, comparing the prediction values of EEMD_LSTM with the least square and autoregressive (LS_AR) model, and International Earth Rotation and Reference Systems Service (IERS) Bulletin A. Results indicate that the EEMD_LSTM model can improve the prediction accuracy of PMX and PMY up to 35.79% and 32.30%, respectively. Additionally, the accuracy of UT1-UTC predicted by using the EEMD_LSTM model demonstrates an improvement of 41.93% in the mid- and long-term (120–365 days). |
| format | Article |
| id | doaj-art-11431342ecdd4ba6b2fb6a2df6e9c423 |
| institution | DOAJ |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-11431342ecdd4ba6b2fb6a2df6e9c4232025-08-20T03:16:00ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532025-06-0111710.1080/10095020.2025.2522154Prediction of polar motion and UT1-UTC based on the hybrid EEMD_LSTM modelChenxiang Wang0Pengfei Zhang1Wenbin Shen2Jiayao Wang3State Key Laboratory of Spatial Datum, College of Remote Sensing and Geoinformatics Engineering, Faculty of Geographical Science and Engineering, Henan University, Zhengzhou, ChinaState Key Laboratory of Spatial Datum, College of Remote Sensing and Geoinformatics Engineering, Faculty of Geographical Science and Engineering, Henan University, Zhengzhou, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan, ChinaState Key Laboratory of Spatial Datum, College of Remote Sensing and Geoinformatics Engineering, Faculty of Geographical Science and Engineering, Henan University, Zhengzhou, ChinaAccurate and real-time Earth rotation parameters (ERPs) are crucial for various scientific fields, including astronomy, geoscience, and oceanography. Although there are various space geodetic techniques, such as VLBI, GNSS, DORIS, and SLR, which can determine the ERPs with high accuracy, these methods have a certain delay and are difficult to meet the needs of real-time applications. Therefore, the ERPs prediction with high accuracy is very significant. Conventional approaches to predicting ERPs are mainly based on the linear models. However, it is not always optimal for the non-linear effects of ERPs. In this paper, we propose a model that combines Ensemble Empirical Mode Decomposition (EEMD) with Long Short-Term Memory (LSTM), denoted as EEMD_LSTM. We make the prediction experiments by using the EEMD_LSTM model, all experiments use the IERS EOP 20C04 as the basic series, the sliding window set as one week, and the prediction accuracies of polar motion (PM) and UT1-UTC from 2011 to 2021 are analyzed. Then, comparing the prediction values of EEMD_LSTM with the least square and autoregressive (LS_AR) model, and International Earth Rotation and Reference Systems Service (IERS) Bulletin A. Results indicate that the EEMD_LSTM model can improve the prediction accuracy of PMX and PMY up to 35.79% and 32.30%, respectively. Additionally, the accuracy of UT1-UTC predicted by using the EEMD_LSTM model demonstrates an improvement of 41.93% in the mid- and long-term (120–365 days).https://www.tandfonline.com/doi/10.1080/10095020.2025.2522154Earth rotation parametersensemble empirical mode decompositionLSTMpolar motionUT1-UTC |
| spellingShingle | Chenxiang Wang Pengfei Zhang Wenbin Shen Jiayao Wang Prediction of polar motion and UT1-UTC based on the hybrid EEMD_LSTM model Geo-spatial Information Science Earth rotation parameters ensemble empirical mode decomposition LSTM polar motion UT1-UTC |
| title | Prediction of polar motion and UT1-UTC based on the hybrid EEMD_LSTM model |
| title_full | Prediction of polar motion and UT1-UTC based on the hybrid EEMD_LSTM model |
| title_fullStr | Prediction of polar motion and UT1-UTC based on the hybrid EEMD_LSTM model |
| title_full_unstemmed | Prediction of polar motion and UT1-UTC based on the hybrid EEMD_LSTM model |
| title_short | Prediction of polar motion and UT1-UTC based on the hybrid EEMD_LSTM model |
| title_sort | prediction of polar motion and ut1 utc based on the hybrid eemd lstm model |
| topic | Earth rotation parameters ensemble empirical mode decomposition LSTM polar motion UT1-UTC |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2025.2522154 |
| work_keys_str_mv | AT chenxiangwang predictionofpolarmotionandut1utcbasedonthehybrideemdlstmmodel AT pengfeizhang predictionofpolarmotionandut1utcbasedonthehybrideemdlstmmodel AT wenbinshen predictionofpolarmotionandut1utcbasedonthehybrideemdlstmmodel AT jiayaowang predictionofpolarmotionandut1utcbasedonthehybrideemdlstmmodel |