Factors determining subsidence in urbanized floodplains: evidence from MT‐InSAR in Seville (southern Spain)
Bibliographic record
Abstract
Abstract Major rivers have traditionally been linked with important human settlements throughout history. The growth of cities over recent river deposits makes necessary the use of multidisciplinary approaches to characterize the evolution of drainage networks in urbanized areas. Since under‐consolidated fluvial sediments are especially sensitive to compaction, their spatial distribution, thickness, and mechanical behavior must be studied. Here, we report on subsidence in the city of Seville (Southern Spain) between 2003 and 2010, through the analysis of the results obtained with the Multi‐Temporal InSAR (MT‐InSAR) technique. In addition, the temporal evolution of the subsidence is correlated with the rainfall, the river water column and the piezometric level. Finally, we characterize the geotechnical parameters of the fluvial sediments and calculate the theoretical settlement in the most representative sectors. Deformation maps clearly indicate that the spatial extent of subsidence is controlled by the distribution of under‐consolidated fine‐grained fluvial sediments at heights comprised in the range of river level variation. This is clearly evident at the western margin of the river and the surroundings of its tributaries, and differs from rainfall results as consequence of the anthropic regulation of the river. On the other hand, this influence is not detected at the eastern margin due to the shallow presence of coarse‐grain consolidated sediments of different terrace levels. The derived results prove valuable for implementing urban planning strategies, and the InSAR technique can therefore be considered as a complementary tool to help unravel the subsidence tendency of cities located over under‐consolidated fluvial deposits. Copyright © 2017 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".