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Mining Subsidence Prediction by Combining Support Vector Machine Regression and Interferometric Synthetic Aperture Radar Data

by Lichun Sui 1,2, Fei Ma 1,* and Nan Chen 1
1
College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China
2
National Administration of Surveying, Mapping, and Geoinformation Engineering Research Center of National Geographic Conditions Monitoring, Xi’an 710054, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(6), 390; https://doi.org/10.3390/ijgi9060390
Received: 9 May 2020 / Revised: 11 June 2020 / Accepted: 13 June 2020 / Published: 15 June 2020
Mining subsidence is time-dependent and highly nonlinear, especially in the Loess Plateau region in Northwestern China. As a consequence, and mainly in building agglomerations, the structures can be damaged severely during or after underground extraction, with risks to human life. In this paper, we propose an approach based on a combination of a differential interferometric synthetic aperture radar (DInSAR) technique and a support vector machine (SVM) regression algorithm optimized by grid search (GS-SVR) to predict mining subsidence in a timely and cost-efficient manner. We consider five Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR) images encompassing the Dafosi coal mine area in Binxian and Changwu counties, Shaanxi Province. The results show that the subsidence predicted by the proposed InSAR and GS-SVR approach is consistent with the Global Positioning System (GPS) measurements. The maximum absolute errors are less than 3.1 cm and the maximum relative errors are less than 14%. The proposed approach combining DInSAR with GS-SVR technology can predict mining subsidence on the Loess Plateau of China with a high level of accuracy. This research may also help to provide disaster warnings. View Full-Text
Keywords: subsidence prediction; interferometric synthetic aperture radar (InSAR); support vector machine regression (SVR) subsidence prediction; interferometric synthetic aperture radar (InSAR); support vector machine regression (SVR)
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Sui, L.; Ma, F.; Chen, N. Mining Subsidence Prediction by Combining Support Vector Machine Regression and Interferometric Synthetic Aperture Radar Data. ISPRS Int. J. Geo-Inf. 2020, 9, 390.

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