Full
Outline
During the mining process of the mine working face, the disturbance of the aquifer can easily lead to an increase in the water inflow of the working face, which threatens the safe production of the mine. Accurate prediction of the water inflow of the working face can effectively prevent water disaster accidents. In this study, the multi-feature prediction method of water inflow in working face based on deep learning model was studied. Combined with the characteristics of water inflow data and the actual situation of the site, the "double aquifer water level+maximum height of microseismic event" was selected as the characteristic data, and the multi-feature prediction model of water inflow in working face of mine based on VMD-iCHOA-GRU was established. At the same time, the prediction effect of multi-feature model was further verified under different feature combinations, single feature conditions and different working face data. The results show that the evaluation indexes MAE, RMSE and MAPE of the water inflow prediction results of the VMD-iCHOA-GRU model are 53.56 m3/d, 62.98 m3/d and 3.1%, respectively, which are better than other control models, and its prediction accuracy is the highest. The prediction accuracy of the multi-feature model is higher than that of the single-factor model and other single-feature and double-feature models in various combinations, and the stronger the correlation between the feature data and the research target, the more conducive it is to improving the prediction accuracy. The prediction effect of the model is good under different working face data, which verifies that the model has relative stability and certain scalability. The research results provide new ideas and new methods for the prediction of water inflow in mine working faces, and have scientific guiding significance and reference value for mine water prevention and control.
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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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