The weir-gate structure has larger discharge capacity. To accurately and efficiently check the discharge of weir-gate, three intelligent algorithms including BP neural network, SVM and GRNN were used to predict the discharge coefficient of cylindrical weir-gate. The correlation analysis and variation law between dimensionless parameters and discharge coefficient were discussed. The results show that the GRNN and the BP can accurately predict the discharge coefficient of the cylindrical weir-gate. The determination coefficient of the BP in the test stage is 0.997, the root mean square error is 0.009, the average absolute percentage is 0.801 %, and the Nash efficiency coefficient is 0.997, which is superior to the GRNN, and it can be used as an efficient and high-precision prediction model for the discharge coefficient of the weir-gate. There is a stronger correlation between the ratio of gate opening to cylinder diameter (a/D), the ratio of weir head to cylinder diameter (Hw/D) and Cd. The Cd increased with the increase of upstream Froude number (Fr) and Hw/D, and the greater the a/D is, the greater the increase of Cd is. The search results provide theoretical reference and technical support for the popularization and application of cylindrical weir gate in practical engineering.
| 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 |