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As wind speed and wave height are the main loading parameters in offshore facility operations, their accurate prediction is of great importance. In order to solve the problem of wind speed and wave height prediction with complex and changeable characteristics, a wave height forecast model was established based on prototype monitoring data and Long-Short-Term Memory (LSTM) neural network. Firstly, the correlation analysis of wind speed and wave height was carried out based on prototype monitoring data. Then, a one-step-ahead wind speed forecast model and wave height forecast method were established based on LSTM neural network. Different prediction models with different time intervals (t=0.5 h, 1 h, 3 h) were built to verify the accuracy. Finally, a joint prediction model based on two forecast models was obtained with a prediction error of only 0.12 m at the time interval of 0.5 h.
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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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