Paper Info
Title
Analysis of the Contribution Rate of the Influencing Factors to Land Subsidence in the Eastern Beijing Plain, China Based on Extremely Randomized Trees (ERT) Method.
Abstract
As a common geological hazard, land subsidence is widely distributed in the Eastern Beijing Plain. The pattern of evolution of this geological phenomenon is controlled by many factors, including groundwater level change in different aquifers, compressible layers of different thicknesses, and static and dynamic loads. First, based on the small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique, we employed 47 ENVISAT ASAR images and 48 RADARSAT-2 images to acquire the ground deformation of the Beijing Plain from June 2003 to November 2015 and then validated the results using leveling benchmark monitoring. Second, we innovatively calculated additional stress to obtain static and dynamic load information. Finally, we evaluated the contribution rate of the influencing factors to land subsidence by using the Spearman's rank correlation coefficient (SRCC) and extremely randomized trees (ERT) machine learning methods. The SBAS-InSAR outcomes revealed that the maximum deformation rate was 110.7 mm/year from 2003 to 2010 and 144.4 mm/year from 2010 to 2015. The SBAS-InSAR results agreed well with the leveling benchmark monitoring results; the correlation coefficients were 0.97 and 0.96 during the 2003-2010 and 2013-2015 periods, respectively. The contribution rate of the second confined aquifer to the cumulative land subsidence was 49.3% from 2003 to 2010, accounting for the largest proportion; however, its contribution rate decreased to 23.4% from 2010 to 2015. The contribution rate of the third confined aquifer to the cumulative land subsidence increased from 2003 to 2015. Although the contribution of additional stress engendered from static and dynamic loads to the cumulative land subsidence was slight, it had a significant effect on the uneven land subsidence, with a contribution rate of 33.8% from 2003 to 2010 and 23.1% from 2010 to 2015. These findings provide scientific support for mitigating hazards associated with land subsidence.
Year
DOI
Venue
2020
10.3390/rs12182963
REMOTE SENSING
Keywords
DocType
Volume
land subsidence,Spearman's rank correlation coefficient (SRCC) method,extremely randomized trees (ERT) method,contribution rate
Journal
12
Issue
Citations 
PageRank 
18
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Fengkai Li100.34
Huili Gong28829.37
Beibei Chen3358.00
Chaofan Zhou400.68
Lin Guo501.35