Gas concentration prediction based on SSA algorithm with CNN-BiLSTM-attention

Accurate prediction of coal mine gas concentration is a crucial prerequisite for preventing gas exceed and disasters. However, the existing methods still suffer from issues such as low data utilization, difficulty in effectively integrating multivariate nonlinear spatiotemporal features, and poor ge...

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Vydáno v:	Scientific reports Ročník 15; číslo 1; s. 34214 - 13
Hlavní autoři:	Xing, Wenjing, Yang, Yanguo, Zhang, Yanxin, Yang, Yong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 01.10.2025 Nature Publishing Group Nature Portfolio
Témata:	639/166 639/4077 639/705 704/172 Accuracy Algorithms Artificial intelligence Attention mechanism Coal mines Concentration prediction Deep learning Foraging behavior Forecasting Gas concentration Humanities and Social Sciences Integrated approach Machine learning Mining multidisciplinary Neural networks Occupational safety Optimization algorithms Predictions Science Science (multidisciplinary) Sparrow optimization algorithm Support vector machines Time series Wind speed Deep learning Sparrow optimization algorithm Attention mechanism Concentration prediction Gas concentration
ISSN:	2045-2322, 2045-2322
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Abstract	Accurate prediction of coal mine gas concentration is a crucial prerequisite for preventing gas exceed and disasters. However, the existing methods still suffer from issues such as low data utilization, difficulty in effectively integrating multivariate nonlinear spatiotemporal features, and poor generalization capability when achieving relatively high prediction accuracy but requiring longer prediction durations. To address these challenges, this study focuses on a tunneling face in a Shanxi coal mine and proposes a novel hybrid deep learning model (CNN-BiLSTM-Attention). The model employs a 1D-CNN to extract local spatial features of gas concentration, temperature, wind speed, rock pressure, and CO concentration, utilizes BiLSTM to model bidirectional temporal dependencies, and incorporates an attention mechanism to dynamically weight critical features, such as sudden changes in gas concentration. Additionally, the sparserow search algorithm (SSA) was applied to automatically optimize hyperparameters, including the number of CNN filters and BiLSTM hidden units. The results demonstrate that the proposed SCBA model achieves an RMSE of 0.0171 and MAPE of 0.084. Compared to mainstream models such as attention-LSTM, SSA-LSTM-aAttention, and rTransformer-LSTM, the RMSE was improved by 23.3%, 4.4%, and 30.2%, respectively, while the MAPE was improved by 38.7%, 14.3%, and 43.62%, respectively, indicating superior prediction accuracy. To validate the contribution of each module, ablation experiments were conducted by sequentially removing the CNN, BiLSTM, and Attention components for error analysis, confirming the necessity of the multi-module collaborative mechanism. In multi-step predictions, the proposed model exhibits better generalization capability as the prediction horizon increases, with the slowest error growth, providing insights for enhancing gas concentration prediction generalization. The model achieves the smallest error at 20 time steps, laying a foundation for subsequent anomaly analysis and offering miners sufficient safety evacuation time, thereby establishing a reliable basis for real-time gas disaster early warning.
AbstractList	Abstract Accurate prediction of coal mine gas concentration is a crucial prerequisite for preventing gas exceed and disasters. However, the existing methods still suffer from issues such as low data utilization, difficulty in effectively integrating multivariate nonlinear spatiotemporal features, and poor generalization capability when achieving relatively high prediction accuracy but requiring longer prediction durations. To address these challenges, this study focuses on a tunneling face in a Shanxi coal mine and proposes a novel hybrid deep learning model (CNN-BiLSTM-Attention). The model employs a 1D-CNN to extract local spatial features of gas concentration, temperature, wind speed, rock pressure, and CO concentration, utilizes BiLSTM to model bidirectional temporal dependencies, and incorporates an attention mechanism to dynamically weight critical features, such as sudden changes in gas concentration. Additionally, the sparserow search algorithm (SSA) was applied to automatically optimize hyperparameters, including the number of CNN filters and BiLSTM hidden units. The results demonstrate that the proposed SCBA model achieves an RMSE of 0.0171 and MAPE of 0.084. Compared to mainstream models such as attention-LSTM, SSA-LSTM-aAttention, and rTransformer-LSTM, the RMSE was improved by 23.3%, 4.4%, and 30.2%, respectively, while the MAPE was improved by 38.7%, 14.3%, and 43.62%, respectively, indicating superior prediction accuracy. To validate the contribution of each module, ablation experiments were conducted by sequentially removing the CNN, BiLSTM, and Attention components for error analysis, confirming the necessity of the multi-module collaborative mechanism. In multi-step predictions, the proposed model exhibits better generalization capability as the prediction horizon increases, with the slowest error growth, providing insights for enhancing gas concentration prediction generalization. The model achieves the smallest error at 20 time steps, laying a foundation for subsequent anomaly analysis and offering miners sufficient safety evacuation time, thereby establishing a reliable basis for real-time gas disaster early warning. Accurate prediction of coal mine gas concentration is a crucial prerequisite for preventing gas exceed and disasters. However, the existing methods still suffer from issues such as low data utilization, difficulty in effectively integrating multivariate nonlinear spatiotemporal features, and poor generalization capability when achieving relatively high prediction accuracy but requiring longer prediction durations. To address these challenges, this study focuses on a tunneling face in a Shanxi coal mine and proposes a novel hybrid deep learning model (CNN-BiLSTM-Attention). The model employs a 1D-CNN to extract local spatial features of gas concentration, temperature, wind speed, rock pressure, and CO concentration, utilizes BiLSTM to model bidirectional temporal dependencies, and incorporates an attention mechanism to dynamically weight critical features, such as sudden changes in gas concentration. Additionally, the sparserow search algorithm (SSA) was applied to automatically optimize hyperparameters, including the number of CNN filters and BiLSTM hidden units. The results demonstrate that the proposed SCBA model achieves an RMSE of 0.0171 and MAPE of 0.084. Compared to mainstream models such as attention-LSTM, SSA-LSTM-aAttention, and rTransformer-LSTM, the RMSE was improved by 23.3%, 4.4%, and 30.2%, respectively, while the MAPE was improved by 38.7%, 14.3%, and 43.62%, respectively, indicating superior prediction accuracy. To validate the contribution of each module, ablation experiments were conducted by sequentially removing the CNN, BiLSTM, and Attention components for error analysis, confirming the necessity of the multi-module collaborative mechanism. In multi-step predictions, the proposed model exhibits better generalization capability as the prediction horizon increases, with the slowest error growth, providing insights for enhancing gas concentration prediction generalization. The model achieves the smallest error at 20 time steps, laying a foundation for subsequent anomaly analysis and offering miners sufficient safety evacuation time, thereby establishing a reliable basis for real-time gas disaster early warning.Accurate prediction of coal mine gas concentration is a crucial prerequisite for preventing gas exceed and disasters. However, the existing methods still suffer from issues such as low data utilization, difficulty in effectively integrating multivariate nonlinear spatiotemporal features, and poor generalization capability when achieving relatively high prediction accuracy but requiring longer prediction durations. To address these challenges, this study focuses on a tunneling face in a Shanxi coal mine and proposes a novel hybrid deep learning model (CNN-BiLSTM-Attention). The model employs a 1D-CNN to extract local spatial features of gas concentration, temperature, wind speed, rock pressure, and CO concentration, utilizes BiLSTM to model bidirectional temporal dependencies, and incorporates an attention mechanism to dynamically weight critical features, such as sudden changes in gas concentration. Additionally, the sparserow search algorithm (SSA) was applied to automatically optimize hyperparameters, including the number of CNN filters and BiLSTM hidden units. The results demonstrate that the proposed SCBA model achieves an RMSE of 0.0171 and MAPE of 0.084. Compared to mainstream models such as attention-LSTM, SSA-LSTM-aAttention, and rTransformer-LSTM, the RMSE was improved by 23.3%, 4.4%, and 30.2%, respectively, while the MAPE was improved by 38.7%, 14.3%, and 43.62%, respectively, indicating superior prediction accuracy. To validate the contribution of each module, ablation experiments were conducted by sequentially removing the CNN, BiLSTM, and Attention components for error analysis, confirming the necessity of the multi-module collaborative mechanism. In multi-step predictions, the proposed model exhibits better generalization capability as the prediction horizon increases, with the slowest error growth, providing insights for enhancing gas concentration prediction generalization. The model achieves the smallest error at 20 time steps, laying a foundation for subsequent anomaly analysis and offering miners sufficient safety evacuation time, thereby establishing a reliable basis for real-time gas disaster early warning. Accurate prediction of coal mine gas concentration is a crucial prerequisite for preventing gas exceed and disasters. However, the existing methods still suffer from issues such as low data utilization, difficulty in effectively integrating multivariate nonlinear spatiotemporal features, and poor generalization capability when achieving relatively high prediction accuracy but requiring longer prediction durations. To address these challenges, this study focuses on a tunneling face in a Shanxi coal mine and proposes a novel hybrid deep learning model (CNN-BiLSTM-Attention). The model employs a 1D-CNN to extract local spatial features of gas concentration, temperature, wind speed, rock pressure, and CO concentration, utilizes BiLSTM to model bidirectional temporal dependencies, and incorporates an attention mechanism to dynamically weight critical features, such as sudden changes in gas concentration. Additionally, the sparserow search algorithm (SSA) was applied to automatically optimize hyperparameters, including the number of CNN filters and BiLSTM hidden units. The results demonstrate that the proposed SCBA model achieves an RMSE of 0.0171 and MAPE of 0.084. Compared to mainstream models such as attention-LSTM, SSA-LSTM-aAttention, and rTransformer-LSTM, the RMSE was improved by 23.3%, 4.4%, and 30.2%, respectively, while the MAPE was improved by 38.7%, 14.3%, and 43.62%, respectively, indicating superior prediction accuracy. To validate the contribution of each module, ablation experiments were conducted by sequentially removing the CNN, BiLSTM, and Attention components for error analysis, confirming the necessity of the multi-module collaborative mechanism. In multi-step predictions, the proposed model exhibits better generalization capability as the prediction horizon increases, with the slowest error growth, providing insights for enhancing gas concentration prediction generalization. The model achieves the smallest error at 20 time steps, laying a foundation for subsequent anomaly analysis and offering miners sufficient safety evacuation time, thereby establishing a reliable basis for real-time gas disaster early warning.
ArticleNumber	34214
Author	Zhang, Yanxin Xing, Wenjing Yang, Yanguo Yang, Yong
Author_xml	– sequence: 1 givenname: Wenjing surname: Xing fullname: Xing, Wenjing organization: College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Mingxiang Campus – sequence: 2 givenname: Yanguo surname: Yang fullname: Yang, Yanguo email: 591009403@qq.com organization: College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Mingxiang Campus – sequence: 3 givenname: Yanxin surname: Zhang fullname: Zhang, Yanxin organization: College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Mingxiang Campus – sequence: 4 givenname: Yong surname: Yang fullname: Yang, Yong organization: College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Mingxiang Campus
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Cites_doi	10.3390/computers12050091 10.1016/j.eswa.2022.116944 10.1016/j.jlp.2023.105082 10.1007/s00477-023-02382-8 10.1016/j.fuel.2023.130462 10.1016/j.egyr.2024.01.026 10.1007/s11831-022-09804-w 10.1016/j.jlp.2011.01.014 10.3390/en16052318 10.1016/j.energy.2022.124889 10.1109/ACCESS.2024.3440631 10.1016/j.energy.2023.130158 10.3390/s22124412 10.1038/s43586-020-00001-2 10.1016/j.ins.2021.11.076 10.1016/j.jhydrol.2022.127445 10.3390/s22228977 10.1016/j.asoc.2020.106912 10.1016/S0925-4005(98)00036-7 10.1016/j.foodres.2024.113958 10.3390/su15043631 10.3390/su142012998 10.1007/978-3-031-73407-6_33 10.1016/j.psep.2021.06.005 10.1007/s42461-022-00654-5 10.1016/j.neucom.2021.03.091 10.1016/j.measurement.2022.112384 10.1016/j.ssci.2021.105420 10.1007/s00603-022-03028-x 10.3390/s21051597 10.1007/s10668-023-03412-9 10.31763/ijrcs.v2i4.888
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References	15838_CR1 C Liu (15838_CR24) 2023; 16 H Zhang (15838_CR18) 2022; 22 Z Niu (15838_CR28) 2021; 452 K Wang (15838_CR22) 2024; 290 J Diaz (15838_CR6) 2023; 37 15838_CR12 K Li (15838_CR23) 2024; 359 N Xu (15838_CR25) 2022; 22 M You (15838_CR15) 2021; 143 P Li (15838_CR2) 2022; 14 K Sharifani (15838_CR9) 2023; 10 H Lin (15838_CR33) 2023; 207 F Gharehchopogh (15838_CR34) 2023; 30 Z Huo (15838_CR16) 2023; 15 R Van de Schoot (15838_CR3) 2021; 1 J Diaz (15838_CR5) 2022; 39 SM Tauseef (15838_CR7) 2011; 24 L Cheng (15838_CR20) 2022; 200 Z Meng (15838_CR13) 2022; 586 S Samantaray (15838_CR10) 2024; 26 MF Aslan (15838_CR30) 2021; 98 F Liu (15838_CR31) 2024; 11 W Zha (15838_CR26) 2022; 260 P Purwono (15838_CR32) 2022; 2 DH Nguyen (15838_CR14) 2022; 606 MAREK (15838_CR4) 2012; 22 MM Taye (15838_CR19) 2023; 12 LS Breda (15838_CR11) 2024; 179 15838_CR21 15838_CR27 H Ishida (15838_CR8) 1998; B49 K Zhang (15838_CR17) 2023; 83 15838_CR29
References_xml	– volume: 12 start-page: 91 issue: 5 year: 2023 ident: 15838_CR19 publication-title: Computers doi: 10.3390/computers12050091 – volume: 200 start-page: 116944 year: 2022 ident: 15838_CR20 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.116944 – volume: 83 start-page: 105082 year: 2023 ident: 15838_CR17 publication-title: J. Loss Prev. Process Ind. doi: 10.1016/j.jlp.2023.105082 – volume: 37 start-page: 2099 year: 2023 ident: 15838_CR6 publication-title: Stoch. Environ. Res. Risk Assess. doi: 10.1007/s00477-023-02382-8 – volume: 359 start-page: 130462 year: 2024 ident: 15838_CR23 publication-title: Fuel doi: 10.1016/j.fuel.2023.130462 – volume: 11 start-page: 1570 year: 2024 ident: 15838_CR31 publication-title: Energy Rep. doi: 10.1016/j.egyr.2024.01.026 – volume: 30 start-page: 427 issue: 1 year: 2023 ident: 15838_CR34 publication-title: Comput. Methods Eng. doi: 10.1007/s11831-022-09804-w – volume: 24 start-page: 371 issue: 4 year: 2011 ident: 15838_CR7 publication-title: J. Loss Prev. Process Ind. doi: 10.1016/j.jlp.2011.01.014 – volume: 16 start-page: 2318 issue: 5 year: 2023 ident: 15838_CR24 publication-title: Energies doi: 10.3390/en16052318 – volume: 260 start-page: 124889 year: 2022 ident: 15838_CR26 publication-title: Energy doi: 10.1016/j.energy.2022.124889 – ident: 15838_CR21 doi: 10.1109/ACCESS.2024.3440631 – volume: 290 start-page: 130158 year: 2024 ident: 15838_CR22 publication-title: Energy doi: 10.1016/j.energy.2023.130158 – volume: 22 start-page: 4412 issue: 12 year: 2022 ident: 15838_CR25 publication-title: Sensors doi: 10.3390/s22124412 – volume: 1 start-page: 1 issue: 1 year: 2021 ident: 15838_CR3 publication-title: Nat. Reviews Methods Primers doi: 10.1038/s43586-020-00001-2 – volume: 10 start-page: 3897 issue: 07 year: 2023 ident: 15838_CR9 publication-title: World Inform. Technol. Eng. J. – volume: 586 start-page: 176 year: 2022 ident: 15838_CR13 publication-title: Inf. Sci. doi: 10.1016/j.ins.2021.11.076 – volume: 606 start-page: 127445 year: 2022 ident: 15838_CR14 publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2022.127445 – volume: 22 start-page: 8977 issue: 22 year: 2022 ident: 15838_CR18 publication-title: Sensors doi: 10.3390/s22228977 – volume: 98 start-page: 106912 year: 2021 ident: 15838_CR30 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.106912 – volume: B49 start-page: 52 issue: 1-2 year: 1998 ident: 15838_CR8 publication-title: Sens. Actuators B: Chem. doi: 10.1016/S0925-4005(98)00036-7 – volume: 179 start-page: 113958 year: 2024 ident: 15838_CR11 publication-title: Food Res. Int. doi: 10.1016/j.foodres.2024.113958 – volume: 15 start-page: 3631 issue: 4 year: 2023 ident: 15838_CR16 publication-title: Sustainability doi: 10.3390/su15043631 – volume: 22 start-page: 477 issue: 2 year: 2012 ident: 15838_CR4 publication-title: Int. J. Appl. Math. Sci. – volume: 14 start-page: 12998 issue: 20 year: 2022 ident: 15838_CR2 publication-title: Sustainability doi: 10.3390/su142012998 – ident: 15838_CR1 doi: 10.1007/978-3-031-73407-6_33 – ident: 15838_CR27 doi: 10.1016/j.psep.2021.06.005 – volume: 39 start-page: 1961 year: 2022 ident: 15838_CR5 publication-title: Min. Metall. Explor. doi: 10.1007/s42461-022-00654-5 – volume: 452 start-page: 48 year: 2021 ident: 15838_CR28 publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.03.091 – volume: 207 start-page: 112384 year: 2023 ident: 15838_CR33 publication-title: Measurement doi: 10.1016/j.measurement.2022.112384 – volume: 143 start-page: 105420 year: 2021 ident: 15838_CR15 publication-title: Saf. Sci. doi: 10.1016/j.ssci.2021.105420 – ident: 15838_CR29 doi: 10.1007/s00603-022-03028-x – ident: 15838_CR12 doi: 10.3390/s21051597 – volume: 26 start-page: 18699 issue: 7 year: 2024 ident: 15838_CR10 publication-title: Environ. Dev. Sustain. doi: 10.1007/s10668-023-03412-9 – volume: 2 start-page: 739 issue: 4 year: 2022 ident: 15838_CR32 publication-title: Int. J. Rob. Control Syst. doi: 10.31763/ijrcs.v2i4.888
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Snippet	Accurate prediction of coal mine gas concentration is a crucial prerequisite for preventing gas exceed and disasters. However, the existing methods still... Abstract Accurate prediction of coal mine gas concentration is a crucial prerequisite for preventing gas exceed and disasters. However, the existing methods...
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StartPage	34214
SubjectTerms	639/166 639/4077 639/705 704/172 Accuracy Algorithms Artificial intelligence Attention mechanism Coal mines Concentration prediction Deep learning Foraging behavior Forecasting Gas concentration Humanities and Social Sciences Integrated approach Machine learning Mining multidisciplinary Neural networks Occupational safety Optimization algorithms Predictions Science Science (multidisciplinary) Sparrow optimization algorithm Support vector machines Time series Wind speed
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Title	Gas concentration prediction based on SSA algorithm with CNN-BiLSTM-attention
URI	https://link.springer.com/article/10.1038/s41598-025-15838-4 https://www.ncbi.nlm.nih.gov/pubmed/41034433 https://www.proquest.com/docview/3256192331 https://www.proquest.com/docview/3256394185 https://doaj.org/article/5f3aee5d4e2a4e2b95af959044e8623d
Volume	15
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