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Noise risk classification of coal mine occupational health based on approximate Markov blanket and GBDT
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Xiaoxu GAO1, 2, Jiake TIAN**, 1, Lu GAO3, Lu DU1, Mengjie FAN1
China Safety Science Journal | 2025, 35(9) : 253 - 262
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China Safety Science Journal | 2025, 35(9): 253-262
Occupational health
Noise risk classification of coal mine occupational health based on approximate Markov blanket and GBDT
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Xiaoxu GAO1, 2, Jiake TIAN**, 1, Lu GAO3, Lu DU1, Mengjie FAN1
Affiliations
  • 1College of Energy and Mining Engineering, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China
  • 2Key Laboratory of Western Mine Exploration and Hazard Prevention Under Ministry of Education, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China
  • 3Shaanxi Jingshen Railway Co., Ltd., Yulin Shaanxi 719000, China
Published: 2025-09-28 doi: 10.16265/j.cnki.issn1003-3033.2025.09.1075
Outline
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To accurately assess the impact of noise on the health of workers in fully mechanized mining face, the key influencing factors of noise occupational health were determined by using the theory of man-machine-environment-management system, combined with Fisher Score, maximum information coefficient and approximate Markov blanket method. The prediction model of coal mine noise risk classification based on the GBDT algorithm was constructed, and the Kappa coefficient and its accuracy were used as the index of model efficiency to compare and verify the accuracy of the model. The results show that the occupational health damage of noise in fully mechanized mining face is closely related to individual status, equipment configuration, environmental factors and occupational health management. Among these, job category, individual age, length of service, protection awareness, degree of equipment automation, pass rate of noise monitoring points, noise exposure, reverberation time and management institutions and personnel are the key indicators of occupational health risk classification and prediction. The accuracy of the occupation health risk classification prediction model of coal mine noise based on GBDT is up to 99.6%, and the average accuracy and Kappa coefficient are 98.3% and 0.958, respectively. The evaluation accuracy of six prediction models for noise occupational health risk classification in fully mechanized mining face is ranked as follows: GBDT > Genetic Algorithm optimization Random Forest(GA-RF) > Particle Swarm optimization Least Squares Support Vector Machine(PSO-LSSVM) > Random Forest(RF) > Support Vector Machine(SVM) > Decision tree.

approximate Markov blanket  /  gradient boosting decision tree (GBDT)  /  coal mine noise  /  noise health risk classification  /  occupational health  /  Fisher Score
Xiaoxu GAO, Jiake TIAN, Lu GAO, Lu DU, Mengjie FAN. Noise risk classification of coal mine occupational health based on approximate Markov blanket and GBDT[J]. China Safety Science Journal, 2025 , 35 (9) : 253 -262 . DOI: 10.16265/j.cnki.issn1003-3033.2025.09.1075
Year 2025 volume 35 Issue 9
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.09.1075
  • Receive Date:2025-04-26
  • Online Date:2026-07-09
  • Published:2025-09-28
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History
  • Received:2025-04-26
  • Revised:2025-07-12
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Affiliations
    1College of Energy and Mining Engineering, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China
    2Key Laboratory of Western Mine Exploration and Hazard Prevention Under Ministry of Education, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China
    3Shaanxi Jingshen Railway Co., Ltd., Yulin Shaanxi 719000, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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Genus
种数
Number of
species
占总种数比例
Percentage of total
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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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