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Assessment model of emergency response capability for coal and gas outburst accidents in mines
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Yun QI1, 2, 3, Kailong XUE2, **, Wei WANG1, 2, 3, Xinchao CUI2, Hongxiang WANG1, Qingjie QI3, 4
China Safety Science Journal | 2024, 34(2) : 225 - 230
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China Safety Science Journal | 2024, 34(2): 225-230
Emergency technology and management
Assessment model of emergency response capability for coal and gas outburst accidents in mines
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Yun QI1, 2, 3, Kailong XUE2, **, Wei WANG1, 2, 3, Xinchao CUI2, Hongxiang WANG1, Qingjie QI3, 4
Affiliations
  • 1 Mechanical Engineering & Automation College,Liaoning University of Technology,Jinzhou Liaoning 121001,China
  • 2 School of Coal Engineering,Shanxi Datong University,Datong Shanxi 037000,China
  • 3 College of Safety Science and Engineering,Liaoning Technical University,Fuxin Liaoning 123000,China
  • 4 China Coal Research Institute,Beijing 100013,China
Published: 2024-02-28 doi: 10.16265/j.cnki.issn1003-3033.2024.02.0983
Outline
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In order to reduce the casualties and property losses and improve the emergency rescue capability in coal and gas outburst accidents,an SSA optimized SVM was proposed to evaluate the emergency rescue capability of coal and gas outburst accidents. First,according to relevant literature and research reports,four first-level indicators,including emergency prevention ability,emergency preparedness ability,emergency response ability and recovery and rehabilitation ability,were constructed. These indicators were further subdivided into 18 second-level indicators,and the score data of each indicator was used as the model training dataset. Then,the network analytic Hierarchy process (ANP) and entropy weight method (EWM) were used to determine the subjective and objective weights of each evaluation indicator under the mutual influence,and the Lagrange function was used to merge the weights to obtain the optimal weights. SSA optimized the radial basis parameters g and penalty factor C of SVM. The result of optimal weight calculation was used as the input of the SSA-SVM model,and the expected value was used as the output for linear regression prediction. Finally,taking a mine in Hebei Province as an example,the prediction results of the SSA-SVM model was compared with the traditional SVM,particle swarm optimization algorithm (PSO) optimization SVM and Whale optimization algorithm (WOA) optimization SVM,and the predicted results and the expected values were analyzed. The results show that the prediction results of the SSA-SVM model are consistent with the reality,and the average absolute error decreases by 8.04%,5.15% and 4.82%,respectively,compared with other models,which proves the superiority of the proposed model. This model can be applied to the evaluation of the emergency rescue ability of coal and gas outburst accidents in actual mines.

coal and gas outburst  /  emergency response capability  /  assessment model  /  sparrow search algorithm (SSA)  /  support vector machine (SVM)  /  combinatorial assignment
Yun QI, Kailong XUE, Wei WANG, Xinchao CUI, Hongxiang WANG, Qingjie QI. Assessment model of emergency response capability for coal and gas outburst accidents in mines[J]. China Safety Science Journal, 2024 , 34 (2) : 225 -230 . DOI: 10.16265/j.cnki.issn1003-3033.2024.02.0983
Year 2024 volume 34 Issue 2
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2024.02.0983
  • Receive Date:2023-08-18
  • Online Date:2025-07-09
  • Published:2024-02-28
Article Data
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History
  • Received:2023-08-18
  • Revised:2023-11-22
Funding
Affiliations
    1 Mechanical Engineering & Automation College,Liaoning University of Technology,Jinzhou Liaoning 121001,China
    2 School of Coal Engineering,Shanxi Datong University,Datong Shanxi 037000,China
    3 College of Safety Science and Engineering,Liaoning Technical University,Fuxin Liaoning 123000,China
    4 China Coal Research Institute,Beijing 100013,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
total species (%)

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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