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Research on Dust Concentration Prediction Model for Underground Mine Based on NRBO-RF
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Fuming GUO1, Shanshan LI2, Chunshan ZHENG1
Mining Research and Development | 2025, 45(6) : 173 - 181
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Mining Research and Development | 2025, 45(6): 173-181
Research on Dust Concentration Prediction Model for Underground Mine Based on NRBO-RF
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Fuming GUO1, Shanshan LI2, Chunshan ZHENG1
Affiliations
  • 1.School of Safety Science and Engineering, Anhui University of Science and Technology, Anhui, Huainan 232001, China
  • 2.School of Economics and Management, Anhui University of Science and Technology, Anhui, Huainan 232001, China
Published: 2025-06-25
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In order to realize the accurate prediction of mine dust concentration, based on the field monitoring data of a mining working face in a coal mine in Shaanxi Province, the random forest (RF) model was used to construct a multi-factor dust concentration prediction model by taking temperature, humidity, wind speed and dust concentration at the current moment as input vectors. Four intelligent optimization algorithms (Newton-Raphson-based optimizer (NRBO), sparrow search algorithm, grey wolf optimizer and whale optimization algorithm) were proposed to optimize the parameters of random forest (number of trees and number of leaf nodes). The model was evaluated by determination coefficient(R2), root mean square error (RMSE) and mean absolute error (MAE). The results show that the optimal model for dust concentration prediction is NRBO-RF model. Compared with RF model, the R2, RMSE and MAE of NRBO-RF model are increased by 0.030 3, 0.572 8 and 0.074 9, respectively, and its fitting ability and prediction accuracy are the highest. The research results can provide a reference for intelligent prediction and prevention and control of mine dust.

Mine dust concentration  /  Random forest  /  Newton-Raphson-based optimizer  /  Prediction model
郭福明, 李姗姗, 郑春山. 基于NRBO-RF的井下粉尘浓度预测模型研究. 矿业研究与开发, 2025 , 45 (6) : 173 -181 .
Fuming GUO, Shanshan LI, Chunshan ZHENG. Research on Dust Concentration Prediction Model for Underground Mine Based on NRBO-RF[J]. Mining Research and Development, 2025 , 45 (6) : 173 -181 .
Appendix
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Year 2025 volume 45 Issue 6
52
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  • Receive Date：2024-08-12
  • Online Date：2026-10-30
  • Published：2025-06-25
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  • Received：2024-08-12
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Affiliations
    1.School of Safety Science and Engineering, Anhui University of Science and Technology, Anhui, Huainan 232001, China
    2.School of Economics and Management, Anhui University of Science and Technology, Anhui, Huainan 232001, China
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表12种不同金属材料的力学参数
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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