Research on coalbed gas content prediction method based on LSSA-BP neural network

HOU Enke; RONG Tongrui; WEI Yongfeng; XIA Bingbing; XIE Xiaoshen

doi:10.13347/j.cnki.mkaq.2023.11.010

Safety in Coal Mines > 2023 > 54(11): 55-61. > DOI: 10.13347/j.cnki.mkaq.2023.11.010

HOU Enke, RONG Tongrui, WEI Yongfeng, et al. Research on coalbed gas content prediction method based on LSSA-BP neural network[J]. Safety in Coal Mines, 2023, 54（11）: 55−61. DOI: 10.13347/j.cnki.mkaq.2023.11.010

Citation:

PDF (938 KB)

Research on coalbed gas content prediction method based on LSSA-BP neural network

HOU Enke^{1, 2,},
RONG Tongrui^{1, 2,},
WEI Yongfeng³,
XIA Bingbing^{1, 2},
XIE Xiaoshen^{1, 2}

1.
School of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
2.
Shaanxi Provincial Key Laboratory of Geological Safeguard for Green Coal Development, Xi’an 710054, China
3.
Shaanxi Coal Huangling Mining Co., Ltd., Huangling 727307, China

More Information

Received Date: August 11, 2022
Available Online: November 21, 2023

Graphical Abstract

Abstract

Abstract

In order to improve the accuracy and reliability of coal seam gas content prediction, this paper proposed the coal seam gas content prediction model (LSSA-BP model) based on Logistic chaotic mapping improved sparrow search algorithm to optimize BP neural network. Firstly, the main control factors of gas content were selected using grey correlation analysis (GRA) as the node number of the input layer of the LSSA-BP prediction model. Then, the sparrow population was initialized by Logistic chaotic mapping to increase the diversity of the population. The problems of slow convergence rate and easy to fall into local minimum of single BP model are solved. Through model application, the prediction results of LSSA-BP, SSA-BP and BP models are compared. The results show that: the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE) of LSSA-BP prediction model were 0.346 9 m³/t, 0.172 1 m³/t, 0.414 9 m³/t and 27.4036%, respectively, which were better than other models. The accuracy and stability of coal seam gas content prediction are improved.
- coalbed gas content,
- BP neural network,
- sparrow search algorithm (SSA),
- Logistic chaos mapping,
- grey correlation analysis (GRA)

FullText(HTML)

References (21)

References

[1]	张子敏. 瓦斯地质学[M]. 徐州: 中国矿业大学出版社, 2009.
[2]	徐刚,王磊,王海涛,等. 基于灰色理论与BP神经网络的煤层瓦斯含量预测方法[J]. 煤炭技术,2019,38(11):82−85. XU Gang, WANG Lei, WANG Haitao, et al. Prediction method of coal seam gas content based on grey theory and BP neural network[J]. Coal Technology, 2019, 38(11): 82−85.
[3]	李长兴,魏国营. 基于灰色理论-BP神经网络的煤层瓦斯含量预测研究[J]. 煤炭技术,2015,34(5):128−131. LI Changxing, WEI Guoying. Research on prediction of coal seam gas content base on gray theory and BP neural network[J]. Coal Technology, 2015, 34(5): 128−131.
[4]	隆清明. 煤层瓦斯含量间接快速测定方法研究[D]. 重庆: 重庆大学, 2018.
[5]	许满贵,高帅帅,曹艳军,等. 基于灰色理论-多元回归分析的瓦斯含量预测[J]. 煤矿安全,2018,49(9):211−214. XU Mangui, GAO Shuaishuai, CAO Yanjun, et al. Forecast of gas content based on gray theory and multiple regression analysis[J]. Safety in Coal Mines, 2018, 49(9): 211−214.
[6]	周鑫隆,章光,吕辰,等. 深部煤层瓦斯含量的差值GM-RBF预测模型及其应用[J]. 安全与环境学报,2017,17(6):2050−2055. ZHOU Xinlong, ZHANG Guang, LYU Chen, et al. A grey model for predicting the gas content in the deep coal seam and its application via the neural network of the difference radial basis function[J]. Journal of Safety and Environment, 2017, 17(6): 2050−2055.
[7]	魏国营,裴蒙. 基于PCA-AHPSO-SVR的煤层瓦斯含量预测研究[J]. 中国安全生产科学技术,2019,15(3):69−74. WEI Guoying, PEI Meng. Prediction of coal seam gas content based on PCA-AHPSO-SVR[J]. Journal of Safety Science and Technology, 2019, 15(3): 69−74.
[8]	林海飞,周捷,高帆,等. 基于特征选择和机器学习融合的煤层瓦斯含量预测[J]. 煤炭科学技术,2021,49(5):44−51. LIN Haifei, ZHOU Jie, GAO Fan, et al. Coal seam gas content prediction based on fusion of feature selection and machine learning[J]. Coal Science and Technology, 2021, 49(5): 44−51.
[9]	吴观茂,黄明,李刚. 基于BP神经网络的瓦斯含量预测[J]. 煤田地质与勘探,2008,36(1):30−33. WU Guanmao, HUANG Ming, LI Gang. Gas content prediction based on BP neural network[J]. Coal Geology & Exploration, 2008, 36(1): 30−33.
[10]	沈金山,王来斌,许继影,等. 基于灰色神经网络预测潘一东矿瓦斯含量[J]. 煤炭技术,2011,30(4):90−92. SHEN Jinshan, WANG Laibin, XU Jiying, et al. Gas content prediction of Panyi east coal mine based on gray neural network[J]. Coal Technology, 2011, 30(4): 90−92.
[11]	刘景艳,王福忠. 基于粒子群神经网络的煤层瓦斯含量预测[J]. 河南理工大学学报(自然科学版),2014,33(6):724−727. LIU Jingyan, WANG Fuzhong. Coal seam gas content prediction based on particle swarm neural network[J]. Journal of Henan Polytechnic University( Natural Science), 2014, 33(6): 724−727.
[12]	曹博,白刚,李辉. 基于PCA-GA-BP神经网络的瓦斯含量预测分析[J]. 中国安全生产科学技术,2015,11(5):84−90. CAO Bo, BAI Gang, LI Hui. Prediction of gas content based on PCA-GA-BP neural network[J]. Journal of Safety Science and Technology, 2015, 11(5): 84−90.
[13]	赵伟,陈培红,曹阳. 基于ACSOA-BP神经网络的瓦斯含量预测模型[J]. 煤矿安全,2022,53(1):174−180. ZHAO Wei, CHEN Peihong, CAO Yang. Prediction model of coal seam gas content based on ACSOA optimized BP neural network[J]. Safety in Coal Mines, 2022, 53(1): 174−180.
[14]	马磊,陆卫东,魏国营. 基于GASA-BP神经网络的煤层瓦斯含量预测方法研究[J]. 中国安全生产科学技术,2022,18(8):59−65. MA Lei, LU Weidong, WEI Guoying. Study on prediction method of coal seam gas content based on GASA-BP neural network[J]. Journal of Safety Science and Technology, 2022, 18(8): 59−65.
[15]	黄敬宇. 融合t分布和Tent混沌映射的麻雀搜索算法研究[D]. 兰州: 兰州大学, 2021.
[16]	XUE Jiankai, SHEN Bo. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8(1): 22−34.
[17]	IBRAHIM Rehab Ali, ELAZIZ Mohamed Abd, LU Songfeng. Chaotic opposition-based grey-wolf optimization algorithm based on differential evolution and disruption operator for global optimization[J]. Expert Systems with Applications, 2018, 108: 1−27. doi: 10.1016/j.eswa.2018.04.028
[18]	吴丁杰,周庆兴,温立书. 基于Logistic混沌映射的改进麻雀算法[J]. 高师理科学刊,2021,41(6):10−15. doi: 10.3969/j.issn.1007-9831.2021.06.003 WU Dingjie, ZHOU Qingxing, WEN Lishu. Improved sparrow algorithm based on Logistic chaos mapping[J]. Journal of Science of Teachers′ College and University, 2021, 41(6): 10−15. doi: 10.3969/j.issn.1007-9831.2021.06.003
[19]	郝天轩,李鹏飞. 基于灰色关联分析-GA-BP模型预测煤层瓦斯含量[J]. 中国矿业,2016,25(11):116−120. doi: 10.3969/j.issn.1004-4051.2016.11.025 HAO Tianxuan, LI Pengfei. Prediction of gas content based on gray correlation analysis-GA-BP neural network[J]. China Mining Magazine, 2016, 25(11): 116−120. doi: 10.3969/j.issn.1004-4051.2016.11.025
[20]	邵毅明,甘元艺,侯雨彤,等. 基于交通流数据修复的GA-RF方法研究[J]. 重庆理工大学学报(自然科学),2021,35(6):29−36. SHAO Yiming, GAN Yuanyi, HOU Yutong, et al. Research on GA-RF method based on traffic flow data restoration[J]. Journal of Chongqing University of Technology( Natural Science), 2021, 35(6): 29−36.
[21]	赵长春,赵亮,王博. 基于改进粒子群算法的RBF神经网络磨机负荷预测研究[J]. 计算机测量与控制,2020,28(6):19−22. ZHAO Changchun, ZHAO Liang, WANG Bo. Research on mill load prediction of RBF network based on improved particle swarm optimization algorithm[J]. Computer Measurement & Control, 2020, 28(6): 19−22.

[1]	QIAO Meiying, SHI Youqiang. Prediction of rock burst hazard level based on index information fusion[J]. Safety in Coal Mines, 2025, 56(2): 118-125. DOI: 10.13347/j.cnki.mkaq.20240242
[2]	Construction of mine intelligent ventilation and prevention system and information platform for Zhuanlongwan Coal Mine[J]. Safety in Coal Mines, 2022, 53(9): 212-220.
[3]	WEN Hu, TANG Rui, LIU Mingyang, WANG Hu, ZHANG Duo, ZHENG Xuezhao, FAN Shixing, CHENG Xiaojiao, JIN Yongfei. Application of Mine life information detector in accident rescue[J]. Safety in Coal Mines, 2022, 53(4): 162-166.
[4]	FENG Wenbin, LI Shunan, TIAN Hao, YANG Xin, MA Chao, YU Chongchong. Images semantic segmentation method based on fusion edge optimization[J]. Safety in Coal Mines, 2022, 53(2): 136-141.
[5]	LIANG Xiaokang, CUI Enwei, WEI Yinshang. Research on coal mine worker safety information management based on block chain technology[J]. Safety in Coal Mines, 2021, 52(2): 235-238.
[6]	LIU Jinglong, WANG Yunjia, YAN Shiyong, DANG Libo, FENG Fang, LI Yi. Multi-source Remote Sensing Fusion Detection of Coal Fire in Eastern Urumchi[J]. Safety in Coal Mines, 2019, 50(8): 158-161.
[7]	ZHANG Baodong. Application of Multi-source Information Fusion and Knowledge Discovery in Coal Mine Gas Monitoring[J]. Safety in Coal Mines, 2017, 48(5): 137-139,143.
[8]	LIU Zhanqian, CHEN Quan. Establishment of Coal Mine Enterprise Safety Risk Information System[J]. Safety in Coal Mines, 2015, 46(1): 227-229.
[9]	DI Zhiqiang. Prediction Method of Gas Emission Based on Multi-source Information Fusion[J]. Safety in Coal Mines, 2014, 45(8): 172-174,178.
[10]	GAO Rong-bin, DENG Teng-lin, YAN Ming, ZHANG Dong-ying. Risk Assessment of Floor Water Inrush in Xin'an Coal Mine Based on Multi-source Information Fusion[J]. Safety in Coal Mines, 2013, 44(9): 201-204.

Cited By

Cited by

Periodical cited type(2)

1.	杨森忠. 软岩巷道锚杆锚固性能试验分析. 内蒙古煤炭经济. 2024(02): 58-60 .
2.	高丹，岳中文，王鹏，岳小磊，闫逸飞，周星源，李杨. 锚固钻孔软弱夹层结构特征智能识别方法. 煤田地质与勘探. 2024(11): 200-210 .

Other cited types(0)

Get Citation

PDF

XML

Article views (56) PDF downloads (16) Cited by(2)

Turn off MathJax

Article Contents

Abstract

References

Research on coalbed gas content prediction method based on LSSA-BP neural network