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
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".