Multi-step prediction of coal mine adit deformation based on time convolutional long short-term memory
Coal main adit is susceptibility to external factors, so it is crucial to monitor and predict its deformation. Based on the application of fiber optic monitoring for adit deformation, A multi-step prediction model is proposed which is built with Ensemble Empirical Mode Decomposition (EEMD) integrate...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Coal Science and Technology
2025-04-01
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| Series: | Meitan kexue jishu |
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| Online Access: | http://www.mtkxjs.com.cn/article/doi/10.12438/cst.2023-1480 |
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| author | Wenli JI Xin DAN Chenyang MA Jing CHAI Yuyi WU Fengqi QIU Wentao LIU Wulin LEI Yongliang LIU |
| author_facet | Wenli JI Xin DAN Chenyang MA Jing CHAI Yuyi WU Fengqi QIU Wentao LIU Wulin LEI Yongliang LIU |
| author_sort | Wenli JI |
| collection | DOAJ |
| description | Coal main adit is susceptibility to external factors, so it is crucial to monitor and predict its deformation. Based on the application of fiber optic monitoring for adit deformation, A multi-step prediction model is proposed which is built with Ensemble Empirical Mode Decomposition (EEMD) integrated with TCN-LSTM deep learning network for adit deformation. Firstly, the monitoring data containing noise is decomposed into several Intrinsic Mode Functions (IMF) components using the EEMD method. Then, the fuzzy entropy of each IMF component series is calculated, and effective IMF components are selected. Finally, the TCN network is used to extract long-term features from different effective component series, while the LSTM network captures nonlinear features. The prediction results of each component are combined. The multi-output strategy is adopted in the training process of the prediction model, and the output is the fiber optic monitoring value for multiple times in the future. Experimental results on different fiber optic grating sensors show that the EEMD combined with fuzzy entropy method can filter out more noise while retaining the roadway deformation information. Compared with existing methods, the proposed prediction method has a coefficient of determination (R2) of 0.99, and the root mean square error (RMSE) and mean absolute error (MAE) are reduced by 3.0%−10.0% and 5.0%−20.0% in single-step prediction, respectively, resulting in more accurate predictions. Under the multi-output strategy, the average R2 of this proposed method for three steps ahead is 0.95, and the RMSE and MAE values of the strain gauge are reduced by at least 75.0% and 31.5%. The RMSE and MAE values of the displacement meter were reduced by at least 50.0% and 66.7%, respectively, while the RMSE and MAE values of the pressure gauge were reduced by at least 85.7% and 62.3%. the proposed prediction method with multi-output strategy has the lowest error accumulation. The EEMD-TCN-LSTM multi-step prediction method for adit deformation provides a technical basis for predicting the deformation of roadway surrounding rock. |
| format | Article |
| id | doaj-art-cf997f81c83b4bd389bccb7638ed8331 |
| institution | Kabale University |
| issn | 0253-2336 |
| language | zho |
| publishDate | 2025-04-01 |
| publisher | Editorial Department of Coal Science and Technology |
| record_format | Article |
| series | Meitan kexue jishu |
| spelling | doaj-art-cf997f81c83b4bd389bccb7638ed83312025-08-20T03:48:06ZzhoEditorial Department of Coal Science and TechnologyMeitan kexue jishu0253-23362025-04-0153417619010.12438/cst.2023-14802023-1480Multi-step prediction of coal mine adit deformation based on time convolutional long short-term memoryWenli JI0Xin DAN1Chenyang MA2Jing CHAI3Yuyi WU4Fengqi QIU5Wentao LIU6Wulin LEI7Yongliang LIU8College School of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege School of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaChina Coal Energy Research Institute Co., Ltd., Xi’an 710000China Coal Energy Research Institute Co., Ltd., Xi’an 710000China Coal Energy Research Institute Co., Ltd., Xi’an 710000School of Energy Engineering, Longdong University, Qingyang 745000, ChinaKey Laboratory of Western Mine Exploitation and Hazard Prevention, Ministry of Education, Xi’an University of Science and Technology, Xi’an 710054, ChinaCoal main adit is susceptibility to external factors, so it is crucial to monitor and predict its deformation. Based on the application of fiber optic monitoring for adit deformation, A multi-step prediction model is proposed which is built with Ensemble Empirical Mode Decomposition (EEMD) integrated with TCN-LSTM deep learning network for adit deformation. Firstly, the monitoring data containing noise is decomposed into several Intrinsic Mode Functions (IMF) components using the EEMD method. Then, the fuzzy entropy of each IMF component series is calculated, and effective IMF components are selected. Finally, the TCN network is used to extract long-term features from different effective component series, while the LSTM network captures nonlinear features. The prediction results of each component are combined. The multi-output strategy is adopted in the training process of the prediction model, and the output is the fiber optic monitoring value for multiple times in the future. Experimental results on different fiber optic grating sensors show that the EEMD combined with fuzzy entropy method can filter out more noise while retaining the roadway deformation information. Compared with existing methods, the proposed prediction method has a coefficient of determination (R2) of 0.99, and the root mean square error (RMSE) and mean absolute error (MAE) are reduced by 3.0%−10.0% and 5.0%−20.0% in single-step prediction, respectively, resulting in more accurate predictions. Under the multi-output strategy, the average R2 of this proposed method for three steps ahead is 0.95, and the RMSE and MAE values of the strain gauge are reduced by at least 75.0% and 31.5%. The RMSE and MAE values of the displacement meter were reduced by at least 50.0% and 66.7%, respectively, while the RMSE and MAE values of the pressure gauge were reduced by at least 85.7% and 62.3%. the proposed prediction method with multi-output strategy has the lowest error accumulation. The EEMD-TCN-LSTM multi-step prediction method for adit deformation provides a technical basis for predicting the deformation of roadway surrounding rock.http://www.mtkxjs.com.cn/article/doi/10.12438/cst.2023-1480adit deformationmulti-step predictiontcn-lstm prediction modelensemble empirical mode decompositioncoal mine intelligence |
| spellingShingle | Wenli JI Xin DAN Chenyang MA Jing CHAI Yuyi WU Fengqi QIU Wentao LIU Wulin LEI Yongliang LIU Multi-step prediction of coal mine adit deformation based on time convolutional long short-term memory Meitan kexue jishu adit deformation multi-step prediction tcn-lstm prediction model ensemble empirical mode decomposition coal mine intelligence |
| title | Multi-step prediction of coal mine adit deformation based on time convolutional long short-term memory |
| title_full | Multi-step prediction of coal mine adit deformation based on time convolutional long short-term memory |
| title_fullStr | Multi-step prediction of coal mine adit deformation based on time convolutional long short-term memory |
| title_full_unstemmed | Multi-step prediction of coal mine adit deformation based on time convolutional long short-term memory |
| title_short | Multi-step prediction of coal mine adit deformation based on time convolutional long short-term memory |
| title_sort | multi step prediction of coal mine adit deformation based on time convolutional long short term memory |
| topic | adit deformation multi-step prediction tcn-lstm prediction model ensemble empirical mode decomposition coal mine intelligence |
| url | http://www.mtkxjs.com.cn/article/doi/10.12438/cst.2023-1480 |
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