Home Journals Articles Updates About
CN
image
收藏切换
Improving SSA and optimizing BPNN for coal gas permeability prediction model
收藏切换
PDF
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
Less
收藏切换
China Safety Science Journal | 2025, 35(2): 137-143
• Safety engineering technology •
Improving SSA and optimizing BPNN for coal gas permeability prediction model
Full
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
Outline
收藏切换

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
Appendix
Less
Year 2025 volume 35 Issue 2
PDF
319
137
Cite this Article
BibTeX
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
Affiliations
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
References
Share
https://castjournals.cast.org.cn/joweb/zgaqkxxb/EN/10.16265/j.cnki.issn1003-3033.2025.02.0552
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
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
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House