DL for CPO feature selection and forecasting

Accurate short-term prediction of Celestial Pole Offsets (CPO), essential for applications such as satellite navigation and space geodesy, remains a significant challenge. We addressed this task using a 1D Convolutional Neural Network (1D CNN), a machine learning model designed to capture temporal patterns effectively. Our objective was to enhance the prediction accuracy of the key CPO components, dX and dY. To improve model interpretability, we employed SHapley Additive exPlanations (SHAP), which identifies the most influential input features, such as historical dX and dY values and data from models like the Free Core Nutation (FCN) model. This transparency is critical for scientific and operational contexts.

We evaluated our model using various input data types, including rapid Earth Orientation Parameters (EOPs), Bulletin A data from the International Earth Rotation and Reference Systems Service (IERS), and FCN-derived data. Our results demonstrated improved prediction accuracy across all data sources, with rapid EOPs emerging as the most effective for short-term forecasts, particularly for the initial day. Leveraging rapid EOPs, we simulated the conditions of the 2nd Earth Orientation Prediction Comparison Campaign (EOPPCC) to further test our approach. The model outperformed other machine learning methods used in the campaign for dX predictions, although dY proved more challenging due to its complex dynamics.

This study highlights the potential of 1D CNNs in advancing CPO forecasting, particularly when coupled with interpretable frameworks like SHAP and diverse, high-quality data sources. Our findings underscore the transformative role of deep learning in enhancing the precision and reliability of Earth Orientation Parameter predictions, thereby supporting critical scientific and operational applications.