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Outline
Currently, the accuracy of medium- and long-term predictions of Earth rotation parameters (ERP) (prediction duration exceeding 30 days) can only reach the milliarcsecond level. This level of accuracy is significantly lower than the microarcsecond level required by astrometry and the millimeter level needed by space geodesy. This paper systematically evaluated the performance of four deep learning models (BP, RNN, LSTM, and Transformer) in short-, medium-, and long-term ERP forecasts, with a focus on long-term predictions. The experimental results show that in 180-day predictions, the precision of polar motion forecasts by the Transformer model is 43.8% higher than that of traditional methods, and the prediction error of length of day (LOD) is reduced to 0.27 ms. In UT1-TAI predictions, the BP model achieved the best accuracy with an RMSE of 7.83 ms. This study can provide reference for solving the medium- and long-term ERP forecasting challenges in the autonomous operation of the Beidou system.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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