TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation Warning
This study addresses the problems of multi-source data redundancy, insufficient feature capture timing, and delayed risk warning in the prediction of gas concentration in fully mechanized coal-mining operations by constructing a three-pronged technical approach that integrates feature dimensionality...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/2571-6255/8/5/175 |
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| author | Yanping Wang Longcheng Zhang Zhenguo Yan Jun Deng Yuxin Huang Zhixin Qin Yuqi Cao Yiyang Wang |
| author_facet | Yanping Wang Longcheng Zhang Zhenguo Yan Jun Deng Yuxin Huang Zhixin Qin Yuqi Cao Yiyang Wang |
| author_sort | Yanping Wang |
| collection | DOAJ |
| description | This study addresses the problems of multi-source data redundancy, insufficient feature capture timing, and delayed risk warning in the prediction of gas concentration in fully mechanized coal-mining operations by constructing a three-pronged technical approach that integrates feature dimensionality reduction, hybrid modeling, and intelligent early warning. First, sparse kernel principal component analysis (SKPCA) is used to accomplish the feature decoupling of multi-source monitoring data, and its optimal dimensionality reduction performance is verified using long-term and short-term neural networks (LSTMs). Second, an innovative TCN–Transformer hybrid architecture is proposed. The transient fluctuation characteristics of gas concentration are captured using causal dilation convolution, while a multi-head self-attention mechanism is used to analyze the cross-scale correlation of geological mining parameters. A flood optimization algorithm (FLA) is used to establish a hyperparameter collaborative optimization framework. Compared to TCN-LSTM, CNN-GRU, and other hybrid models, the hybrid model proposed in this study exhibits superior point prediction performance, with a maximum R<sup>2</sup> of 0.980988. Finally, a dynamic confidence interval is established using the locally weighted kernel density estimation (LWD-KDE) method with an optimized bandwidth, and an unsupervised early warning mechanism for the risk of gas concentration fluctuations in coal mines is constructed. The results provide a comprehensive approach to preventing and controlling gas disasters in fully mechanized mining operations. This research effectively promotes the transformation and upgrading of coal-mine-safety-monitoring systems to an active defense paradigm. |
| format | Article |
| id | doaj-art-fe815a0287a44f9f81ae3c5a4b68eb0b |
| institution | Kabale University |
| issn | 2571-6255 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fire |
| spelling | doaj-art-fe815a0287a44f9f81ae3c5a4b68eb0b2025-08-20T03:47:54ZengMDPI AGFire2571-62552025-04-018517510.3390/fire8050175TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation WarningYanping Wang0Longcheng Zhang1Zhenguo Yan2Jun Deng3Yuxin Huang4Zhixin Qin5Yuqi Cao6Yiyang Wang7College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaThis study addresses the problems of multi-source data redundancy, insufficient feature capture timing, and delayed risk warning in the prediction of gas concentration in fully mechanized coal-mining operations by constructing a three-pronged technical approach that integrates feature dimensionality reduction, hybrid modeling, and intelligent early warning. First, sparse kernel principal component analysis (SKPCA) is used to accomplish the feature decoupling of multi-source monitoring data, and its optimal dimensionality reduction performance is verified using long-term and short-term neural networks (LSTMs). Second, an innovative TCN–Transformer hybrid architecture is proposed. The transient fluctuation characteristics of gas concentration are captured using causal dilation convolution, while a multi-head self-attention mechanism is used to analyze the cross-scale correlation of geological mining parameters. A flood optimization algorithm (FLA) is used to establish a hyperparameter collaborative optimization framework. Compared to TCN-LSTM, CNN-GRU, and other hybrid models, the hybrid model proposed in this study exhibits superior point prediction performance, with a maximum R<sup>2</sup> of 0.980988. Finally, a dynamic confidence interval is established using the locally weighted kernel density estimation (LWD-KDE) method with an optimized bandwidth, and an unsupervised early warning mechanism for the risk of gas concentration fluctuations in coal mines is constructed. The results provide a comprehensive approach to preventing and controlling gas disasters in fully mechanized mining operations. This research effectively promotes the transformation and upgrading of coal-mine-safety-monitoring systems to an active defense paradigm.https://www.mdpi.com/2571-6255/8/5/175TCNtransformerSKPCAFLAdynamic kernel density estimationrisk warning |
| spellingShingle | Yanping Wang Longcheng Zhang Zhenguo Yan Jun Deng Yuxin Huang Zhixin Qin Yuqi Cao Yiyang Wang TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation Warning Fire TCN transformer SKPCA FLA dynamic kernel density estimation risk warning |
| title | TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation Warning |
| title_full | TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation Warning |
| title_fullStr | TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation Warning |
| title_full_unstemmed | TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation Warning |
| title_short | TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation Warning |
| title_sort | tcn transformer spatio temporal feature decoupling and dynamic kernel density estimation for gas concentration fluctuation warning |
| topic | TCN transformer SKPCA FLA dynamic kernel density estimation risk warning |
| url | https://www.mdpi.com/2571-6255/8/5/175 |
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