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Improving SSA and optimizing BPNN for coal gas permeability prediction model
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Wei WANG1, 2, 3, Xinchao CUI4, **, Yun QI1, 2, 5, Xuping LI1, 2, 5, Huangrui WANG4, Qingjie QI6
China Safety Science Journal | 2025, 35(2) : 137 - 143
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China Safety Science Journal | 2025, 35(2): 137-143
• Safety engineering technology •
Improving SSA and optimizing BPNN for coal gas permeability prediction model
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Wei WANG1, 2, 3, Xinchao CUI4, **, Yun QI1, 2, 5, Xuping LI1, 2, 5, Huangrui WANG4, Qingjie QI6
Affiliations
  • 1 School of Mining and Coal，Inner Mongolia University of Science and Technology，Baotou Inner Mongolia 014010，China
  • 2 Inner Mongolia Key Laboratory of Mining Engineering，Baotou Inner Mongolia 014010，China
  • 3 Inner Mongolia Research Center for Coal Safety Mining and Utilization Engineering and Technology，Baotou Inner Mongolia 014010，China
  • 4 School of Coal Engineering，Shanxi Datong University，Datong Shanxi 037000，China
  • 5 Inner Mongolia Cooperative Innovation Center for Coal Green Mining and Green Utilization，Baotou Inner Mongolia 014010，China
  • 6 Liaoning Institute of Science and Engineering，Jinzhou Liaoning 121000，China
Published: 2025-02-28 doi: 10.16265/j.cnki.issn1003-3033.2025.02.0552
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In order to predict coal gas permeability more accurately and ensure coal mine safety production，a prediction model of coal gas permeability based on ISSA-optimized BPNN was constructed. Firstly，the sparrow search algorithm (SSA) was improved by introducing Sine chaotic mapping and Gaussian mutation to enhance its global search capability and local optimization accuracy，thereby optimizing the weight and threshold configuration of BPNN. Secondly，the data on the factors affecting gas permeability were processed using Pearson correlation coefficient matrix and kernel principal component analysis (KPCA) to improve the computational efficiency and accuracy of the model. Three principal components with a cumulative variance of 88.59% were extracted as model inputs，and permeability was used as the output for the experiment. Finally，the model was applied to a coal mine in Shanxi for case verification. The experimental results show that ISSA-BPNN outperforms PSO-BPNN，PSO-SVM，PSO-LSSVM，and SSA-BPNN models in four indicators: mean absolute error (MAE)，mean absolute percentage error (MAPE)，root mean square error (RMSE)，root mean square error (RMSE)，and coefficient of determination (R2). Compared with other models，ISSA-BPNN has reduced MAE by 0.032 7，0.022，0.017 9，and 0.018 2 in the test samples，respectively. MAPE decreases by 5.15%，3.14%，2.76%，and 2.36% respectively. RMSE decreases by 0.031 6，0.027 9，0.018 8，and 0.022 2 respectively. R2 increases by 0.077 5，0.065 8，0.040 1，and 0.049 3，respectively. Finally，the case verification shows that its reliability and stability are high in practical applications.

improved sparrow search algorithm (ISSA)  /  back propagation neural network (BPNN)  /  coal gas  /  permeability  /  prediction model
Wei WANG, Xinchao CUI, Yun QI, Xuping LI, Huangrui WANG, Qingjie QI. Improving SSA and optimizing BPNN for coal gas permeability prediction model[J]. China Safety Science Journal, 2025 , 35 (2) : 137 -143 . DOI: 10.16265/j.cnki.issn1003-3033.2025.02.0552
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Year 2025 volume 35 Issue 2
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.02.0552
  • Receive Date：2024-09-25
  • Online Date：2025-07-05
  • Published：2025-02-28
Article Data
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History
  • Received：2024-09-25
  • Revised：2024-11-26
Funding
Affiliations
    1 School of Mining and Coal，Inner Mongolia University of Science and Technology，Baotou Inner Mongolia 014010，China
    2 Inner Mongolia Key Laboratory of Mining Engineering，Baotou Inner Mongolia 014010，China
    3 Inner Mongolia Research Center for Coal Safety Mining and Utilization Engineering and Technology，Baotou Inner Mongolia 014010，China
    4 School of Coal Engineering，Shanxi Datong University，Datong Shanxi 037000，China
    5 Inner Mongolia Cooperative Innovation Center for Coal Green Mining and Green Utilization，Baotou Inner Mongolia 014010，China
    6 Liaoning Institute of Science and Engineering，Jinzhou Liaoning 121000，China
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表12种不同金属材料的力学参数
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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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