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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 34214 - 13
Hauptverfasser:	Xing, Wenjing, Yang, Yanguo, Zhang, Yanxin, Yang, Yong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 01.10.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

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
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/41034433$$D View this record in MEDLINE/PubMed
BookMark	eNp9kUtPGzEUhS1EVZ5_oAs0Ehs2bv1M7CVElCIFWISurTv2nXSiyTjYE1X99zUZChULLNm-sr5zbN9zRPb72CMhXzj7ypk037Li2hrKhKZcG2mo2iOHgilNhRRi_7_6gJzmvGJlaGEVt5_JgSoWSkl5SO5uIFc-9h77IcHQxr7aJAyt35U1ZAxVKRaLywq6ZUzt8Gtd_S5rNbu_p1ftfPF4R2EYirwITsinBrqMpy_7Mfn5_fpx9oPOH25uZ5dz6pU0AxUgG8MD88yAVtaGRtgQEIyxNasDV4gQpiqEwCammdYGUNfTaYPGS21qL4_J7egbIqzcJrVrSH9chNbtDmJaOkhD6zt0upGAqINCAWXWVkNjtWVKoZkIGYrXxei1SfFpi3lw6zZ77DroMW6zk0JPZOmb0QU9f4eu4jb15ac7ilshJS_U2Qu1rdcYXp_3r-kFECPgU8w5YfOKcOaew3VjuK6E63bhOlVEchTlAvdLTG93f6D6C_NvpPs
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
ContentType	Journal Article
Copyright	The Author(s) 2025 2025. The Author(s). The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: The Author(s) 2025 – notice: 2025. The Author(s). – notice: The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	C6C AAYXX CITATION NPM 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU COVID DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 DOA
DOI	10.1038/s41598-025-15838-4
DatabaseName	Springer Nature OA Free Journals CrossRef PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One Community College Coronavirus Research Database ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Biological Sciences ProQuest Health & Medical Collection PML(ProQuest Medical Library) Science Database (ProQuest) Biological Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef PubMed Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic CrossRef PubMed Publicly Available Content Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Biology
EISSN	2045-2322
EndPage	13
ExternalDocumentID	oai_doaj_org_article_5f3aee5d4e2a4e2b95af959044e8623d 41034433 10_1038_s41598_025_15838_4
Genre	Journal Article
GrantInformation_xml	– fundername: Innovative Research Group Project of the National Natural Science Foundation of China grantid: 5227041202 funderid: https://doi.org/10.13039/100014718 – fundername: Innovative Research Group Project of the National Natural Science Foundation of China grantid: 5227041202
GroupedDBID	0R~ 4.4 53G 5VS 7X7 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD AASML ABDBF ABUWG ACGFS ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M1P M2P M7P M~E NAO OK1 PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RNT RNTTT RPM SNYQT UKHRP AAYXX AFFHD CITATION NPM 3V. 7XB 88A 8FK COVID K9. M48 PKEHL PQEST PQUKI PRINS Q9U 7X8
ID	FETCH-LOGICAL-c438t-2a3f81d0c08a5499df29ddea889b0bd14eead74ddd068f7b8ae5b77fe8c358bc3
IEDL.DBID	DOA
ISICitedReferencesCount	0
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001586640100030&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2045-2322
IngestDate	Mon Oct 13 19:20:18 EDT 2025 Mon Oct 06 16:42:19 EDT 2025 Mon Oct 06 17:20:45 EDT 2025 Sun Oct 05 01:50:33 EDT 2025 Sat Nov 29 07:18:12 EST 2025 Thu Oct 02 04:42:56 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	1
Keywords	Deep learning Sparrow optimization algorithm Attention mechanism Concentration prediction Gas concentration
Language	English
License	2025. The Author(s).
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c438t-2a3f81d0c08a5499df29ddea889b0bd14eead74ddd068f7b8ae5b77fe8c358bc3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
OpenAccessLink	https://doaj.org/article/5f3aee5d4e2a4e2b95af959044e8623d
PMID	41034433
PQID	3256192331
PQPubID	2041939
PageCount	13
ParticipantIDs	doaj_primary_oai_doaj_org_article_5f3aee5d4e2a4e2b95af959044e8623d proquest_miscellaneous_3256394185 proquest_journals_3256192331 pubmed_primary_41034433 crossref_primary_10_1038_s41598_025_15838_4 springer_journals_10_1038_s41598_025_15838_4
PublicationCentury	2000
PublicationDate	2025-10-01
PublicationDateYYYYMMDD	2025-10-01
PublicationDate_xml	– month: 10 year: 2025 text: 2025-10-01 day: 01
PublicationDecade	2020
PublicationPlace	London
PublicationPlace_xml	– name: London – name: England
PublicationTitle	Scientific reports
PublicationTitleAbbrev	Sci Rep
PublicationTitleAlternate	Sci Rep
PublicationYear	2025
Publisher	Nature Publishing Group UK Nature Publishing Group Nature Portfolio
Publisher_xml	– name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio
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
SSID	ssj0000529419
Score	2.4604478
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...
SourceID	doaj proquest pubmed crossref springer
SourceType	Open Website Aggregation Database Index Database Publisher
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
SummonAdditionalLinks	– databaseName: Science Database (ProQuest) dbid: M2P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5BAYkL70egoCBxA6uJHxv7hNqKwoFdVdoi9Wb52VaC7LLZIvHv8TjerRCPC4dIVmI5TmbGM54ZfwPwesKiNdx74pLxSrhglJh2Iki0jUtyHhS1MReb6GYzeXqqjovDbShplZs1MS_UfuHQR77Hkm5Ga4S175bfCFaNwuhqKaFxHW4ky6bFlK4pPd76WDCKxVtVzso0TO4NSV_hmTIqSIsBQ8J_0UcZtv9PtuZvcdKsfo7u_u_E78GdYnjW-yOn3IdroX8At8ZSlD8ewvSDGWqHZxj7AqRbL1cYxMlN1HW-To35fL82X87S-OvzrzU6cevD2YwcXHyan0wJYnXm7MlH8Pno_cnhR1JKLRDHmVwTalhMlmvjGmlwx-gjVWnhM1Iq21jf8pA4rks09c1Exs5KE4TtuhikY0Jaxx7DTr_ow1OoWROSUhSWygmiC8ZEbWcQZayLKrTMV_Bm88P1ckTU0DkSzqQeyaMTeXQmj-YVHCBNtj0RDTvfWKzOdBEuLSIzIQjPAzXpskqYqIRqOA9pw4av3N2QRhcRHfQVXSp4tX2chAsjJqYPi8uxD1OI71PBk5ETtjPhLaIlMlbB2w1rXA3-9w969u-5PIfbNHMnpgvuws56dRlewE33fX0xrF5m9v4JyKIAYQ priority: 102 providerName: ProQuest
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
WOSCitedRecordID	wos001586640100030&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M7P dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: 7X7 dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M2P dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3db9MwED_BBtJeEN8ERhUk3sBa4o_aflynDZBoFNEhlSfLTmyoBOnUdpP47_E5aRkCxAsPOUWJFdn3s33nnP07gJdjFpzlbUua6LwSLhglthwLElzRxHHuNXUhJZuQVaXmc11fS_WFe8J6euBecUciMOu9aLmnNl5OCxu00AXnPjrjrMXZt5D62mKqZ_Wmmpd6OCVTMHW0jpYKT5NRQUoMFRL-iyVKhP1_8jJ_i5Amw3N2F-4MHmN-3Nf0Htzw3X243eeQ_P4Apm_sOm_w8GE3MODmFyuMvqRbNFJtHm9ms-Pcfv28XC02X77l-Pc1P6kqMlm8n51PCZJspm2PD-Hj2en5yVsy5EggDWdqQ6hlIbqcRVMoi0u9NlAdZyyrlHaFa0vuY1eREYy2GKsgnbJeOCmDVw0TyjXsEex1y84_gZwVPloz4agaIy1giDA1FunBZNC-ZG0Gr7b6Mhc9FYZJIWymTK9dE7VrknYNz2CCKt2VRBrr9CCCawZwzb_AzeBwC4gZxtbasOiloV_Kygxe7F7HUYGhDtv55WVfhmkk5sngcQ_kria8RJpDxjJ4vUX258f_3qCn_6NBz-CApi6IuwEPYW-zuvTP4VZztVmsVyO4KecySTWC_clpVX8YpX4d5ZTWKGWU-_W7af3pB9rA-eY
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VAoILb0qgQJDgRK0mfmycA0JtobTqblRpF6k3Yyd2Wwmyy2YL6p_iN-Jxkq0Qj1sPHCJZieXYyTcPe-xvAF4OmDOaVxUpvfNKuGCU6HQgiDNJ6eXc5tS4kGwiKwp5dJQfrsCP_iwMbqvsdWJQ1NW0xDXyTeZtM3ojLH07-0owaxRGV_sUGi0sDuz5dz9la97sv_P_9xWlu-8nO3ukyypASs7kglDNnHfSkjKRGidHlaO5l3EtZW4SU6Xc-o-b-e5XyUC6zEhthckyZ2XJhDQl8-1egascmcVwqyA9XK7pYNSMp3l3NidhcrPx9hHPsFFBUgxQEv6L_QtpAv7k2_4Wlw3mbvf2__ah7sCtzrGOt1pJuAsrtr4H19tUm-f3YfRBN3GJZzTrjig4ns0xSBWKaMur2BfG461Yfz7241mcfIlxkTreKQqyfTocT0YEuUjD7tAH8PFSxvIQVutpbR9BzBLrjb4wVA6QPdF5NJcaWdQyl9uUVRG87n-wmrWMISpE-plULRyUh4MKcFA8gm3EwLImsn2HG9P5seqUhxKOaWtFxS3V_jK50C4XecK59RNSfOV6DwXVqaBGXeAgghfLx155YERI13Z61tZhOfIXRbDWIm_ZE54iGyRjEWz0ULxo_O8DevzvvjyHG3uT0VAN94uDJ3CTBsnArZHrsLqYn9mncK38tjht5s-CaMXw6bIh-hP3umEz
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Zb9QwEB6VLVS8cB-BAkGCJ7A2sZ2N84BQDxZW7UYrbZHaJ-MkdlsJsstmC-pf49cxk2MrxPHWBx4iWYnl-PjmsGc8A_BiIFxmZFGwHJVXJiPBmQkHEXNZkCOd24Rnrk42EaepOjxMJmvwo7sLQ26VHU-sGXUxy-mMvC9QNpM2IsK-a90iJrvDt_OvjDJIkaW1S6fRQGTPnn_H7Vv1ZrSLa_2S8-G7g50PrM0wwHIp1JJxIxwqbEEeKEMbpcLxBOndKJVkQVaE0uJExziUIhgoF2fK2CiLY2dVLiKV5QLbvQLrqJJL3oP1yWg8OVqd8JANTYZJe1MnEKpfobSkG208YiGZK5n8RRrWSQP-pOn-ZqWthd_w5v88bbfgRqty-1sNjdyGNVvegWtNEs7zuzB-byo_p9ubZRtC2J8vyHxVF0nKFz4WptMt33w-xvEsT774dHzt76Qp2z7dnx6MGUUprf1G78HHSxnLfeiVs9I-BF8EFtWBKONqQHEVHeI8NxRfLXaJDUXhwatusfW8iSWiax8AoXQDDY3Q0DU0tPRgm_CwqklxwOsXs8WxbtmKjpww1kaFtNzgkyWRcUmUBFJa3KrSLzc7WOiWOVX6AhMePF99RrZCtiJT2tlZU0ckFNnIgwcNClc9kSHFiRTCg9cdLC8a__uAHv27L89gA5Gp90fp3mO4zmsiIZ_JTegtF2f2CVzNvy1Pq8XTls58-HTZGP0J7cNrfA
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Gas+concentration+prediction+based+on+SSA+algorithm+with+CNN-BiLSTM-attention&rft.jtitle=Scientific+reports&rft.au=Wenjing+Xing&rft.au=Yanguo+Yang&rft.au=Yanxin+Zhang&rft.au=Yong+Yang&rft.date=2025-10-01&rft.pub=Nature+Portfolio&rft.eissn=2045-2322&rft.volume=15&rft.issue=1&rft.spage=1&rft.epage=13&rft_id=info:doi/10.1038%2Fs41598-025-15838-4&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_5f3aee5d4e2a4e2b95af959044e8623d
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon