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Outline
In order to improve the prediction accuracy of multi-source index risk identification in goaf, based on the particle swarm optimization (PSO) algorithm to optimize the input weights and thresholds of the extreme learning machine (ELM), a PSO-ELM method was proposed to identify the risk of multi-source indicators in the goaf. Nine indicators, including mining depth, mining method, goaf height, the maximum exposed area, the maximum exposed height, the maximum exposed span, mining pillars, measured volume, and control rate, which affect the risk of goaf in a mine in South China, were used as the main input values, and 135 measured goaf data were used as an example to predict the hazard level of goaf. The research results show that the prediction accuracy of radial basis function and traditional extreme learning machine is 88% and 92%, respectively, while the prediction accuracy of the extreme learning machine model optimized by particle swarm optimization algorithm is 96%. Compared with radial basis functions and traditional extreme learning machines, the PSO-ELM algorithm performs better on the test sets, has a better stability and generalization ability, possess good guiding significance and practicality, and is suitable for practical engineering applications.
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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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