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.

<p>Polar Motion is the movement of the Earth's rotational axis relative to its crust, reflecting the influence of the material exchange and mess redistribution of each layer of the Earth on the Earth's rotation axis.<br />The real-time estimation of Polar Motion (PM) is needed for the navigation of Earth satellites and interplanetary spacecraft. However, it is impossible to have real-time information due to the complexity of the measurement model and data processing.</p> <p>Various prediction methods have been developed. However, the accuracy of PM prediction is still not satisfactory even for a few days in the future. Therefore, a new technique or a combination of the existing methods needs to be investigated for improving the accuracy of the prediction PM.<br />In this study, we combine the 1D  Convolutional Neural Network with the Singular Spectrum Analysis (SSA).<br /> The computational strategy follows multiple steps, first, we model the predominant trend of the PM time series using SSA. Then, the difference between the PM time series and its SSA estimation is modeled using the 1D Convolution Neural Network. However, we developed a Multivariate Multi step 1D-CNN Model with a Multi-output strategy to predict at the same time both components (Xp, Yp)  of the PM.  . We introduce to the  Model: the Ocean Angular Momentum, Atmospheric Angular Momentum, and Hydrological Angular Momentum (OAM+AAM+HAM) to improve the results. Multiple sets of PM predictions which range between 1 and 10 days have been performed based on an IERS 14 C04 time series to assess the capability of our hybrid Model. Our results illustrate that the proposed method can efficiently predict both (Xp, Yp) of PM.</p>

<p>Abstract<br>Knowledge of the Earth orientation parameters (EOP) is essential for numerous practical and scientific applications including, positioning and navigating in space and on Earth.<br>The LOD (length of day), which represents the variation in the Earth's rotation rate, is the most difficult to forecast since it is primarily affected by the torques associated with changes in atmospheric circulation. Therefore, accurate LOD predictions are an ongoing challenge and are the focus of this work.<br>Consequently, there is a compelling need to identify next-generation time series prediction algorithms to be integrated into an operational processing chain. Of specific interest is the emergence of deep learning methods.  These methods tend to behave as highly adaptive and versatile fitting algorithms and can thus replace conventional fitting functions for enabling more accurate predictions.<br>In this study, the 1D-Convolutional Neural Networks (1D-CNN) is introduced to model and to predict the LOD using the IERS EOP 14 C04 and the axial Z component of the atmospheric angular momentum (AAM) 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 by using the LS method, then, we obtain the residuals series of each one to be used in the 1D-CNN prediction algorithm. Finally,  we analyzed the results before and after introducing the AAM function. These results prove the potential of the proposed method as an optimal algorithm to successfully reconstruct and predict LOD for up to 7 days.<br>Keywords</p><p>1D-Convolutional Neural Networks (1D-CNN);  Length of the day;  atmospheric angular momentum(AAM) function; prediction</p>

<p>The real-time Earth orientation parameters (EOP) estimation is needed for many applications, including precise tracking and navigation of interplanetary spacecraft, climate forecasting, and disaster prevention. However, the complexity and time-consuming data processing always lead to time delays. Accordingly, several methods were developed and applied for the EOP prediction. However, the accuracy of EOP prediction is still not satisfactory even for prediction of just a few days in the future. Therefore, new methods or a combination of the existing approaches can be investigated to improve the predicted EOP. To assess the various EOP prediction capabilities, the international Earth rotation and reference systems service (IERS) established the working group on the 2nd Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC).<br>Our EOP prediction team provides the full set of EOP predictions weekly for one year ahead. The SSA+Copula method and the empirical free core nutation (FCN) model (named B16) are used for Earth rotation parameters and celestial pole offsets (CPO) prediction, respectively. <br>Our preliminary results illustrate an improvement in EOP prediction compared to the current EOP prediction methods, especially on CPO. Additionally, the comparison with other method results indicates that the proposed techniques can efficiently and precisely predict the EOP at different terms (short, mid, and long term).</p>