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Seepage analysis is the key research content of dam safety and stability, and it is of great significance for dam disaster risk control by constructing a high-precision prediction model of seepage quantity for earth-rock dam. In order to further improve the seepage prediction capability of earth-rock dam, a prediction model combining long short-term memory neural(LSTM) networks, convolutional neural(CNN) networks, and attention mechanism (Attention) was proposed. Firstly, CNN was used to mine the deep features of the data, then the time series features of the seepage flow monitoring data was extracted through LSTM, and finally the attention mechanism to the pooling layer and the fully connected layer was added to determine the importance of different time features and assign weights. Through the application analysis of engineering examples, compared with CNN, LSTM and CNN-LSTM models, the CNN-LSTM-Attention model has better prediction effect, and its coefficient of determination R2 is as high as more than 0.98, and it can capture the spatial characteristics and temporal dependence of seepage data at the same time, which shows strong reliability and stability in the prediction of seepage flow of earth-rock dam.
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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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