Research on Prediction of Surface Deformation in Mining Areas Based on TPE-Optimized Integrated Models and Multi-Temporal InSAR
The prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/23/5546 |
| _version_ | 1827592131807543296 |
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| author | Sichun Long Maoqi Liu Chaohui Xiong Tao Li Wenhao Wu Hongjun Ding Liya Zhang Chuanguang Zhu Shide Lu |
| author_facet | Sichun Long Maoqi Liu Chaohui Xiong Tao Li Wenhao Wu Hongjun Ding Liya Zhang Chuanguang Zhu Shide Lu |
| author_sort | Sichun Long |
| collection | DOAJ |
| description | The prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance of data, the underlying information within these data has yet to be fully unearthed. Consequently, this paper advocates a novel methodology for prognosticating mining area surface deformation by integrating ensemble learning with MT-InSAR technology. Initially predicated upon the MT-InSAR monitoring outcomes, the target variables for the ensemble learning dataset were procured by melding distance-based features with spatial autocorrelation theory. In the ensuing phase, spatial stratified sampling alongside mutual information methodologies were deployed to select the features of the dataset. Utilizing the MT-InSAR monitoring data from the Zixing coal mine in Hunan, China, the relationship between fault slippage and coal extraction in the study area was rigorously analyzed using Granger causality tests and Johansen cointegration assays, thereby acquiring the dataset requisite for training the Bagging model. Subsequently, leveraging the Bagging technique, ensemble models were constructed employing Decision Trees, Support Vector Regression, and Multi-layer Perceptron as foundational estimators. Furthermore, the Tree-structured Parzen Estimator (TPE) optimization algorithm was applied to the Bagging model, resulting in an optimal model for predicting fault slip in mining areas. In comparison with the baseline model, the performance increased by 25.88%, confirming the effectiveness of the data preprocessing method outlined in this study. This result also demonstrates the innovation and feasibility of combining ensemble learning with MT-InSAR technology for predicting mining area surface deformation. This investigation is the first to integrate TPE-optimized ensemble models with MT-InSAR technology, offering a new perspective for predicting surface deformation in mining territories and providing valuable insights for further uncovering the hidden information in MT-InSAR monitoring data. |
| first_indexed | 2024-03-09T01:43:24Z |
| format | Article |
| id | doaj.art-c7e3dc2dc7ac4d72b52e5a8156b9eb72 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T01:43:24Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c7e3dc2dc7ac4d72b52e5a8156b9eb722023-12-08T15:24:58ZengMDPI AGRemote Sensing2072-42922023-11-011523554610.3390/rs15235546Research on Prediction of Surface Deformation in Mining Areas Based on TPE-Optimized Integrated Models and Multi-Temporal InSARSichun Long0Maoqi Liu1Chaohui Xiong2Tao Li3Wenhao Wu4Hongjun Ding5Liya Zhang6Chuanguang Zhu7Shide Lu8School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaSchool of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaSchool of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaSatellite Navigation and Positioning Technology Research Centre, Wuhan University, Wuhan 430079, ChinaSchool of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaChina Construction Fifth Engineering Division Corp., Ltd., Changsha 410000, ChinaSchool of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaSchool of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaChina Construction Fifth Engineering Division Corp., Ltd., Changsha 410000, ChinaThe prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance of data, the underlying information within these data has yet to be fully unearthed. Consequently, this paper advocates a novel methodology for prognosticating mining area surface deformation by integrating ensemble learning with MT-InSAR technology. Initially predicated upon the MT-InSAR monitoring outcomes, the target variables for the ensemble learning dataset were procured by melding distance-based features with spatial autocorrelation theory. In the ensuing phase, spatial stratified sampling alongside mutual information methodologies were deployed to select the features of the dataset. Utilizing the MT-InSAR monitoring data from the Zixing coal mine in Hunan, China, the relationship between fault slippage and coal extraction in the study area was rigorously analyzed using Granger causality tests and Johansen cointegration assays, thereby acquiring the dataset requisite for training the Bagging model. Subsequently, leveraging the Bagging technique, ensemble models were constructed employing Decision Trees, Support Vector Regression, and Multi-layer Perceptron as foundational estimators. Furthermore, the Tree-structured Parzen Estimator (TPE) optimization algorithm was applied to the Bagging model, resulting in an optimal model for predicting fault slip in mining areas. In comparison with the baseline model, the performance increased by 25.88%, confirming the effectiveness of the data preprocessing method outlined in this study. This result also demonstrates the innovation and feasibility of combining ensemble learning with MT-InSAR technology for predicting mining area surface deformation. This investigation is the first to integrate TPE-optimized ensemble models with MT-InSAR technology, offering a new perspective for predicting surface deformation in mining territories and providing valuable insights for further uncovering the hidden information in MT-InSAR monitoring data.https://www.mdpi.com/2072-4292/15/23/5546surface deformationensemble learningmulti-temporal InSARbaggingcausality test |
| spellingShingle | Sichun Long Maoqi Liu Chaohui Xiong Tao Li Wenhao Wu Hongjun Ding Liya Zhang Chuanguang Zhu Shide Lu Research on Prediction of Surface Deformation in Mining Areas Based on TPE-Optimized Integrated Models and Multi-Temporal InSAR Remote Sensing surface deformation ensemble learning multi-temporal InSAR bagging causality test |
| title | Research on Prediction of Surface Deformation in Mining Areas Based on TPE-Optimized Integrated Models and Multi-Temporal InSAR |
| title_full | Research on Prediction of Surface Deformation in Mining Areas Based on TPE-Optimized Integrated Models and Multi-Temporal InSAR |
| title_fullStr | Research on Prediction of Surface Deformation in Mining Areas Based on TPE-Optimized Integrated Models and Multi-Temporal InSAR |
| title_full_unstemmed | Research on Prediction of Surface Deformation in Mining Areas Based on TPE-Optimized Integrated Models and Multi-Temporal InSAR |
| title_short | Research on Prediction of Surface Deformation in Mining Areas Based on TPE-Optimized Integrated Models and Multi-Temporal InSAR |
| title_sort | research on prediction of surface deformation in mining areas based on tpe optimized integrated models and multi temporal insar |
| topic | surface deformation ensemble learning multi-temporal InSAR bagging causality test |
| url | https://www.mdpi.com/2072-4292/15/23/5546 |
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