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In view of the fact that traditional neural network models can hardly make full use of the topological relationship of the backward forward information of dam deformation monitoring time series data, while the bidirectional long short-term memory (BiLSTM) can effectively learn the backward forward information, a combined dam deformation prediction model BES-BiLSTM was proposed based on bald eagle search algorithm optimized bidirectional long short-term memory neural network. Firstly, the bald eagle search algorithm was used to optimize parameters of the model. Secondly, the bidirectional learning feature of BiLSTM was used to train the model to enhance the correlation between the data. Then, the settlement value of a concrete dam hydropower station was taken as example 1 for dam deformation prediction based on the BES-BiLSTM model. Another concrete dam horizontal displacement value was taken as example 2 to verify the model performance. Finally, the prediction results of the BES-BiLSTM model were studied in comparison with those of the traditional long and short term memory neural network (LSTM) model and the BiLSTM model. The results show that the BES-BiLSTM model has stronger fitting and prediction capabilities than the single traditional LSTM and BiLSTM models, which can be used for deformation prediction of concrete dams and slopes.
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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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