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Support Vector Machine (SVM) has advantages in small sample simulation prediction, but there is subjectivity in the selection of penalty factor C and kernel function parameter γ in SVM. Therefore, the Harris Hawks Optimization (HHO) algorithm was used to optimize C and γ in the SVM. And then the HHO-SVM mode was established to predict water quality in the Xiyuan tunnel section of Lake Dianchi Caohai. The results show that the prediction accuracy of the water quality prediction model based on HHO-SVM is higher than that of the SVM based on genetic algorithm (GASVM) and the SVM based on whale optimization algorithm (WOA-SVM). It is proved that the HHO is feasible to optimize the parameters in SVM, and HHO-SVM can be used in water quality prediction.
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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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