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Outline
Aiming at the problems of low accuracy of empirical models and meteorological parameter model accuracy limited by meteorological parameters in commonly used ZTD prediction models, we propose a regional ZTD prediction model based on improved multi-head attention mechanism combined with long short-term memory network (LSTM-MHA). The model is constructed using data from the third-generation global pressure and temperature model (GPT3), along with station latitude, longitude, elevation, day of year, and hour of day. Using ZTD data from 26 continuously operating reference stations (CORS) in Europe in 2023 as reference values, comparative experiments were conducted with the GPT3 model, LSTM model, and CNN model. The results show that the LSTM-MHA model achieves a mean bias close to 0, with root mean square error and mean absolute error of 13.43 mm and 12.4 mm, respectively. The accuracy of LSTM-MHA model has been improved by 60.8%, 55.2%, and 53.3% compared to the GPT3, CNN, and LSTM models. Evaluation of the LSTM-MHA model performance under different distances and prediction lengths reveals that the proposed method performs stably in both sparse station networks and long-term forecasting scenarios, demonstrating its suitability for regional multi-station prediction tasks.
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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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