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On Time-Series InSAR by SA-SVR Algorithm: Prediction and Analysis of Mining Subsidence
Joint Authors
Meng, Xin
Li, Qianwen
Shi, Yun
Zhang, Tongkang
Shi, Jingjian
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-04
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Given the increasingly serious geological disasters caused by underground mining in the Hancheng mining area in China and the existing problems with mining subsidence prediction models, this article uses the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology to process 109 Sentinel-1A images of this mining area from December 2015 to February 2020.
The results show that there are three subsidences: one in Donganshang, one in south of Zhuyuan village, and one in Shandizhaizi village.
In the basin, the maximum annual average subsidence rate is 300 mm/a, and the maximum cumulative subsidence is 1000 mm.
The SBAS-InSAR results are compared with Global Positioning System (GPS) observation results, and the correlation coefficient is 74%.
Finally, a simulated annealing (SA) algorithm is used to estimate the optimal parameters of a support vector regression (SVR) prediction model, which is applied for mining subsidence prediction.
The prediction results are compared with the results of SVR and the GM (1, 1).
The minimum value of the coefficient of determination for prediction with SA-SVR model is 0.57, which is significantly better than that those of the other two prediction methods.
The results indicate that the proposed prediction model offers high subsidence prediction accuracy and fully meets the requirements of engineering applications.
American Psychological Association (APA)
Shi, Yun& Li, Qianwen& Meng, Xin& Zhang, Tongkang& Shi, Jingjian. 2020. On Time-Series InSAR by SA-SVR Algorithm: Prediction and Analysis of Mining Subsidence. Journal of Sensors،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1190669
Modern Language Association (MLA)
Shi, Yun…[et al.]. On Time-Series InSAR by SA-SVR Algorithm: Prediction and Analysis of Mining Subsidence. Journal of Sensors No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1190669
American Medical Association (AMA)
Shi, Yun& Li, Qianwen& Meng, Xin& Zhang, Tongkang& Shi, Jingjian. On Time-Series InSAR by SA-SVR Algorithm: Prediction and Analysis of Mining Subsidence. Journal of Sensors. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1190669
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190669