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In order to enhance the real-time flood forecasting accuracy in the Linyi River Basin, a TOPKAPI grid model was developed based on the underlying surface characteristics of the Linyi River Basin. The TOPKAPI model simulation results were corrected at different lead times using BP neural networks and LSTM models. Furthermore, a stacking approach was applied, employing the Transformer model as a secondary learning tool to refine the corrections made by BP and LSTM. The results indicate that after real-time correction with the BP and LSTM models, the improvement of the simulation accuracy of the TOPKAPI model is obvious, with better correction results for shorter lead times. Following the stacking method for secondary learning, the correction results is the best, effectively enhancing the flood forecasting accuracy in the Linyi River Basin.
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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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