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Aiming at the evaluation of water resources security in China, combined with the characteristics that support vector machine (SVM) has good classification effect on small samples and nonlinear problems, the sparrow search algorithm (SSA) was used to optimize the penalty factor (C) and kernel function parameters (g) of the SVM. The support vector machine model optimized by the sparrow search algorithm (SSA-SVM) was used for regional water resources security assessment. A case study was carried out in a certain area of Luoyang City. The results show that the evaluation grade obtained by SSA-SVM method and T-S fuzzy neural network method are basically consistent, the SSA-SVM model has the characteristics of fast searching speed, and not easy to fall into local optimum, which can be used for regional water resources security evaluation.
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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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