Mine water inflow prediction model based on variational mode decomposition and gated recurrent units optimized by improved chimp optimization algorithm

Water damage accidents occur frequently in mines in China, and accurate prediction of incoming water has become an important guarantee for the safe and efficient mining of coal resources. To improve the accuracy of mine water prediction, this paper proposes the VMD-iCHOA-GRU mine water prediction mo...

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Published in:	Scientific reports Vol. 15; no. 1; pp. 4378 - 18
Main Authors:	Chen, Juntao, Fan, Mingjin
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 05.02.2025 Nature Publishing Group Nature Portfolio
Subjects:	639/166/986 639/705/258 704/2151/241 Accuracy Algorithms Coal mining Decomposition Gated recurrent units Humanities and Social Sciences Improved chimp optimization algorithm Mine drainage Mining accidents & safety multidisciplinary Optimization algorithms Prediction models Science Science (multidisciplinary) Variational mode decomposition Water damage accidents Water inrush Water damage accidents Variational mode decomposition Gated recurrent units Improved chimp optimization algorithm
ISSN:	2045-2322, 2045-2322
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Abstract	Water damage accidents occur frequently in mines in China, and accurate prediction of incoming water has become an important guarantee for the safe and efficient mining of coal resources. To improve the accuracy of mine water prediction, this paper proposes the VMD-iCHOA-GRU mine water prediction model by selecting and improving it according to the previous research results in decomposition method, time series prediction model and optimization algorithm. After processing the raw data and setting the model parameters, MAE, RMSE, MAPE and R 2 are selected as the evaluation indexes of prediction accuracy, and VMD-GRU model, iCHOA-GRU model, CHOA-GRU model and GRU model are selected as the comparison models to validate the advantages of the VMD-iCHOA-GRU model in the prediction of mine inrush water. The results show that the VMD-iCHOA-GRU model has the best prediction effect on the trend of water inflow, with the evaluation index values of 0.00862, 0.01059, 0.02189%, 0.87079, respectively, and with the smallest MAE, RMSE, MAPE, and the largest R 2 , and the highest prediction accuracy of the VMD-iCHOA-GRU model.
AbstractList	Abstract Water damage accidents occur frequently in mines in China, and accurate prediction of incoming water has become an important guarantee for the safe and efficient mining of coal resources. To improve the accuracy of mine water prediction, this paper proposes the VMD-iCHOA-GRU mine water prediction model by selecting and improving it according to the previous research results in decomposition method, time series prediction model and optimization algorithm. After processing the raw data and setting the model parameters, MAE, RMSE, MAPE and R2 are selected as the evaluation indexes of prediction accuracy, and VMD-GRU model, iCHOA-GRU model, CHOA-GRU model and GRU model are selected as the comparison models to validate the advantages of the VMD-iCHOA-GRU model in the prediction of mine inrush water. The results show that the VMD-iCHOA-GRU model has the best prediction effect on the trend of water inflow, with the evaluation index values of 0.00862, 0.01059, 0.02189%, 0.87079, respectively, and with the smallest MAE, RMSE, MAPE, and the largest R2, and the highest prediction accuracy of the VMD-iCHOA-GRU model. Water damage accidents occur frequently in mines in China, and accurate prediction of incoming water has become an important guarantee for the safe and efficient mining of coal resources. To improve the accuracy of mine water prediction, this paper proposes the VMD-iCHOA-GRU mine water prediction model by selecting and improving it according to the previous research results in decomposition method, time series prediction model and optimization algorithm. After processing the raw data and setting the model parameters, MAE, RMSE, MAPE and R2 are selected as the evaluation indexes of prediction accuracy, and VMD-GRU model, iCHOA-GRU model, CHOA-GRU model and GRU model are selected as the comparison models to validate the advantages of the VMD-iCHOA-GRU model in the prediction of mine inrush water. The results show that the VMD-iCHOA-GRU model has the best prediction effect on the trend of water inflow, with the evaluation index values of 0.00862, 0.01059, 0.02189%, 0.87079, respectively, and with the smallest MAE, RMSE, MAPE, and the largest R2, and the highest prediction accuracy of the VMD-iCHOA-GRU model.Water damage accidents occur frequently in mines in China, and accurate prediction of incoming water has become an important guarantee for the safe and efficient mining of coal resources. To improve the accuracy of mine water prediction, this paper proposes the VMD-iCHOA-GRU mine water prediction model by selecting and improving it according to the previous research results in decomposition method, time series prediction model and optimization algorithm. After processing the raw data and setting the model parameters, MAE, RMSE, MAPE and R2 are selected as the evaluation indexes of prediction accuracy, and VMD-GRU model, iCHOA-GRU model, CHOA-GRU model and GRU model are selected as the comparison models to validate the advantages of the VMD-iCHOA-GRU model in the prediction of mine inrush water. The results show that the VMD-iCHOA-GRU model has the best prediction effect on the trend of water inflow, with the evaluation index values of 0.00862, 0.01059, 0.02189%, 0.87079, respectively, and with the smallest MAE, RMSE, MAPE, and the largest R2, and the highest prediction accuracy of the VMD-iCHOA-GRU model. Water damage accidents occur frequently in mines in China, and accurate prediction of incoming water has become an important guarantee for the safe and efficient mining of coal resources. To improve the accuracy of mine water prediction, this paper proposes the VMD-iCHOA-GRU mine water prediction model by selecting and improving it according to the previous research results in decomposition method, time series prediction model and optimization algorithm. After processing the raw data and setting the model parameters, MAE, RMSE, MAPE and R 2 are selected as the evaluation indexes of prediction accuracy, and VMD-GRU model, iCHOA-GRU model, CHOA-GRU model and GRU model are selected as the comparison models to validate the advantages of the VMD-iCHOA-GRU model in the prediction of mine inrush water. The results show that the VMD-iCHOA-GRU model has the best prediction effect on the trend of water inflow, with the evaluation index values of 0.00862, 0.01059, 0.02189%, 0.87079, respectively, and with the smallest MAE, RMSE, MAPE, and the largest R 2 , and the highest prediction accuracy of the VMD-iCHOA-GRU model. Water damage accidents occur frequently in mines in China, and accurate prediction of incoming water has become an important guarantee for the safe and efficient mining of coal resources. To improve the accuracy of mine water prediction, this paper proposes the VMD-iCHOA-GRU mine water prediction model by selecting and improving it according to the previous research results in decomposition method, time series prediction model and optimization algorithm. After processing the raw data and setting the model parameters, MAE, RMSE, MAPE and R2 are selected as the evaluation indexes of prediction accuracy, and VMD-GRU model, iCHOA-GRU model, CHOA-GRU model and GRU model are selected as the comparison models to validate the advantages of the VMD-iCHOA-GRU model in the prediction of mine inrush water. The results show that the VMD-iCHOA-GRU model has the best prediction effect on the trend of water inflow, with the evaluation index values of 0.00862, 0.01059, 0.02189%, 0.87079, respectively, and with the smallest MAE, RMSE, MAPE, and the largest R2, and the highest prediction accuracy of the VMD-iCHOA-GRU model. Water damage accidents occur frequently in mines in China, and accurate prediction of incoming water has become an important guarantee for the safe and efficient mining of coal resources. To improve the accuracy of mine water prediction, this paper proposes the VMD-iCHOA-GRU mine water prediction model by selecting and improving it according to the previous research results in decomposition method, time series prediction model and optimization algorithm. After processing the raw data and setting the model parameters, MAE, RMSE, MAPE and R are selected as the evaluation indexes of prediction accuracy, and VMD-GRU model, iCHOA-GRU model, CHOA-GRU model and GRU model are selected as the comparison models to validate the advantages of the VMD-iCHOA-GRU model in the prediction of mine inrush water. The results show that the VMD-iCHOA-GRU model has the best prediction effect on the trend of water inflow, with the evaluation index values of 0.00862, 0.01059, 0.02189%, 0.87079, respectively, and with the smallest MAE, RMSE, MAPE, and the largest R , and the highest prediction accuracy of the VMD-iCHOA-GRU model.
ArticleNumber	4378
Author	Fan, Mingjin Chen, Juntao
Author_xml	– sequence: 1 givenname: Juntao orcidid: 0000-0002-7507-0981 surname: Chen fullname: Chen, Juntao organization: College of Energy and Mining Engineering, Shandong University of Science and Technology, National Demonstration Centre for Experimental Mining Engineering Education, Shandong University of Science and Technology – sequence: 2 givenname: Mingjin orcidid: 0009-0002-7691-1786 surname: Fan fullname: Fan, Mingjin email: 15216335378@163.com organization: College of Energy and Mining Engineering, Shandong University of Science and Technology
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Cites_doi	10.1016/j.engappai.2024.108103 10.1016/j.apenergy.2021.118438 10.1016/j.ins.2020.05.090 10.1109/TGRS.2024.3452937 10.1016/j.scitotenv.2020.137290 10.1371/journal.pone.0308474 10.1016/j.ijmst.2022.04.001 10.1016/j.jclepro.2023.138557 10.1038/s41598-021-82121-7 10.1016/j.jclepro.2023.140411 10.1016/j.eswa.2024.123171 10.1016/j.jhydrol.2024.130725 10.1109/JIOT.2019.2937110 10.1016/j.asoc.2023.111181 10.1016/j.jhydrol.2023.129319 10.3390/s22124628 10.1016/j.eswa.2020.113338 10.11834/jrs.20221408 10.1016/j.ecolind.2021.107975 10.1016/j.imavis.2024.104918 10.1016/j.jacc.2024.03.056 10.1073/pnas.2310012121 10.1016/j.compgeo.2024.106106 10.1109/TSP.2013.2288675 10.1016/j.autcon.2024.105297
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Keywords	Water damage accidents Variational mode decomposition Gated recurrent units Improved chimp optimization algorithm
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References	YF Zeng (82580_CR11) 2023; 51 YQ Zhang (82580_CR23) 2024; 83 SQ Meng (82580_CR29) 2024; 167 JL Li (82580_CR33) 2022; 50 YK Yang (82580_CR3) 2024; 436 WQ Zhang (82580_CR8) 2023; 422 YP Song (82580_CR17) 2024; 246 ZL Li (82580_CR31) 2021; 47 JT Chen (82580_CR2) 2024; 52 YY Chen (82580_CR28) 2024; 152 J Yang (82580_CR40) 2020; 540 Q Wu (82580_CR7) 2021; 11 S Ge (82580_CR22) 2023; 27 G Zhou (82580_CR39) 2022; 22 KM Islam (82580_CR26) 2024; 142 C Feng (82580_CR18) 2022; 310 SN Dong (82580_CR14) 2022; 40 82580_CR21 82580_CR43 YF Zeng (82580_CR4) 2023; 48 YF Zeng (82580_CR5) 2024; 630 K Dragomiretskiy (82580_CR42) 2014; 62 G Sun (82580_CR37) 2020; 7 W Jin (82580_CR38) 2024; 80 SX Yin (82580_CR13) 2023; 51 Z Wang (82580_CR19) 2021; 129 MY Liang (82580_CR30) 2023; 54 EK Hou (82580_CR35) 2023; 23 SM Wang (82580_CR1) 2023; 48 H Liu (82580_CR32) 2023; 51 YF Zeng (82580_CR10) 2023; 619 G Tejasree (82580_CR27) 2024; 27 YF Zeng (82580_CR15) 2022; 47 H Yin (82580_CR36) 2024; 62 YX Zhang (82580_CR24) 2024; 133 Q He (82580_CR44) 2023; 38 S Matarneh (82580_CR25) 2024; 160 JT Chen (82580_CR9) 2023; 51 JH Liu (82580_CR12) 2022; 32 S Ryali (82580_CR16) 2024; 121 N Mashru (82580_CR41) 2024; 19 YF Zeng (82580_CR6) 2023; 51 A Gribov (82580_CR20) 2020; 722 F Wang (82580_CR34) 2022; 44
References_xml	– volume: 133 start-page: 108103 year: 2024 ident: 82580_CR24 publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2024.108103 – volume: 310 year: 2022 ident: 82580_CR18 publication-title: Appl. Energy doi: 10.1016/j.apenergy.2021.118438 – volume: 80 start-page: 3523 issue: 3 year: 2024 ident: 82580_CR38 publication-title: Comput. Mater. Contin. – volume: 540 start-page: 117 year: 2020 ident: 82580_CR40 publication-title: Inf. Sci. doi: 10.1016/j.ins.2020.05.090 – volume: 62 start-page: 4510417 year: 2024 ident: 82580_CR36 publication-title: IEEE Trans. Geosci. Remote Sens doi: 10.1109/TGRS.2024.3452937 – volume: 44 start-page: 143 issue: 3 year: 2022 ident: 82580_CR34 publication-title: Mining Res. Dev. – volume: 48 start-page: 2599 issue: 7 year: 2023 ident: 82580_CR1 publication-title: J. China Coal. Soc. – volume: 722 year: 2020 ident: 82580_CR20 publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2020.137290 – volume: 52 start-page: 189 issue: 3 year: 2024 ident: 82580_CR2 publication-title: Coal. Sci. Technol. – volume: 19 issue: 8 year: 2024 ident: 82580_CR41 publication-title: PLoS ONE doi: 10.1371/journal.pone.0308474 – volume: 47 start-page: 904 issue: 8 year: 2021 ident: 82580_CR31 publication-title: J. Beijing Univ. Technol. – volume: 32 start-page: 513 issue: 3 year: 2022 ident: 82580_CR12 publication-title: Int. J. Min. Sci. Technol. doi: 10.1016/j.ijmst.2022.04.001 – volume: 50 start-page: 149 issue: 4 year: 2022 ident: 82580_CR33 publication-title: Coal. Sci. Technol. – volume: 54 start-page: 56 issue: 5 year: 2023 ident: 82580_CR30 publication-title: Saf. Coal. Mines – volume: 422 year: 2023 ident: 82580_CR8 publication-title: J. Cleaner Prod. doi: 10.1016/j.jclepro.2023.138557 – volume: 11 start-page: 2621 issue: 1 year: 2021 ident: 82580_CR7 publication-title: Sci. Rep. doi: 10.1038/s41598-021-82121-7 – volume: 51 start-page: 24 issue: 7 year: 2023 ident: 82580_CR13 publication-title: Coal Sci. Technol. – volume: 436 year: 2024 ident: 82580_CR3 publication-title: J Clean Prod. doi: 10.1016/j.jclepro.2023.140411 – volume: 246 year: 2024 ident: 82580_CR17 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2024.123171 – volume: 51 start-page: 13 issue: 6 year: 2023 ident: 82580_CR32 publication-title: Coal. Geol. Explor. – volume: 630 year: 2024 ident: 82580_CR5 publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2024.130725 – volume: 7 start-page: 5760 issue: 7 year: 2020 ident: 82580_CR37 publication-title: IEEE Internet Th. J. doi: 10.1109/JIOT.2019.2937110 – volume: 40 start-page: 1 issue: 2 year: 2022 ident: 82580_CR14 publication-title: Mine Water Environ. – volume: 152 year: 2024 ident: 82580_CR28 publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2023.111181 – volume: 23 start-page: 12012 issue: 28 year: 2023 ident: 82580_CR35 publication-title: Sci. Technol. Eng. – volume: 619 year: 2023 ident: 82580_CR10 publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2023.129319 – volume: 51 start-page: 1 issue: 7 year: 2023 ident: 82580_CR6 publication-title: Coal. Sci. Technol. – volume: 22 start-page: 4628 issue: 12 year: 2022 ident: 82580_CR39 publication-title: Sens doi: 10.3390/s22124628 – volume: 51 start-page: 62 issue: 10 year: 2023 ident: 82580_CR11 publication-title: Coal. Geol. Explor. – ident: 82580_CR43 doi: 10.1016/j.eswa.2020.113338 – volume: 27 start-page: 2796 issue: 12 year: 2023 ident: 82580_CR22 publication-title: Natl. Remote Sens Bull. doi: 10.11834/jrs.20221408 – volume: 27 start-page: 52 issue: 1 year: 2024 ident: 82580_CR27 publication-title: Egpt. J. Remote Sens Space Sci. – volume: 38 start-page: 354 issue: 2 year: 2023 ident: 82580_CR44 publication-title: Control Decis. – volume: 47 start-page: 3091 issue: 8 year: 2022 ident: 82580_CR15 publication-title: J. China Coal. Soc. – volume: 129 year: 2021 ident: 82580_CR19 publication-title: Ecol Indic doi: 10.1016/j.ecolind.2021.107975 – volume: 142 year: 2024 ident: 82580_CR26 publication-title: Image Vis. Comput. doi: 10.1016/j.imavis.2024.104918 – volume: 83 start-page: S22 issue: 16S year: 2024 ident: 82580_CR23 publication-title: J. Am. Coll. Cardiol. doi: 10.1016/j.jacc.2024.03.056 – volume: 121 start-page: e2310012121 issue: 09 year: 2024 ident: 82580_CR16 publication-title: Proc. Natl. Acad. Sci. US A doi: 10.1073/pnas.2310012121 – volume: 48 start-page: 3776 issue: 10 year: 2023 ident: 82580_CR4 publication-title: J. China Coal. Soc. – volume: 51 start-page: 179 issue: 7 year: 2023 ident: 82580_CR9 publication-title: Coal Sci. Technol. – volume: 167 year: 2024 ident: 82580_CR29 publication-title: Comput. Geotech. doi: 10.1016/j.compgeo.2024.106106 – volume: 62 start-page: 531 issue: 3 year: 2014 ident: 82580_CR42 publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2013.2288675 – ident: 82580_CR21 – volume: 160 year: 2024 ident: 82580_CR25 publication-title: Autom. Constr. doi: 10.1016/j.autcon.2024.105297
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Snippet	Water damage accidents occur frequently in mines in China, and accurate prediction of incoming water has become an important guarantee for the safe and... Abstract Water damage accidents occur frequently in mines in China, and accurate prediction of incoming water has become an important guarantee for the safe...
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SubjectTerms	639/166/986 639/705/258 704/2151/241 Accuracy Algorithms Coal mining Decomposition Gated recurrent units Humanities and Social Sciences Improved chimp optimization algorithm Mine drainage Mining accidents & safety multidisciplinary Optimization algorithms Prediction models Science Science (multidisciplinary) Variational mode decomposition Water damage accidents Water inrush
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Title	Mine water inflow prediction model based on variational mode decomposition and gated recurrent units optimized by improved chimp optimization algorithm
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