A GPS-based monitoring program of land subsidence due to groundwater withdrawal in Iran
Bibliographic record
Abstract
Land subsidence, lowering of the land surface by mass movement, has been caused by human activities in many countries all over the world. The full economic impact of man-induced subsidence is large, yet difficult to estimate. Groundwater withdrawal is one of the most important causes of land subsidence that has caused extremely expensive damages to buildings, walls, roads, railroads, pipelines, and casings of the water wells. A necessary step to perform a proper analysis of land subsidence is to obtain accurate measurements of actual subsidence at certain intervals. The objective of this paper is to evaluate land subsidence using global positioning system (GPS) technique. One example of subsiding areas is the Rafsanjan plain, which has had the most subsidence in Iran. First, the latest situation of land subsidence in the Rafsanjan plain as well as the geological, hydrogeological conditions and groundwater utilization are explained. Next, the monitoring program and engineering works for its implementation are discussed. Finally, the results of two successive measurements carried out recently as the first attempt in Iran to monitor land subsidence by using GPS are presented and interpreted. Based on the results obtained, it was found that the relationship between the decline of groundwater level and land subsidence is not exactly or necessarily linear at every point. Also, the response of different points of the soil body within the plain would not be the same due to the groundwater withdrawal and the change in groundwater level. The ground behavior is influenced by many other factors such as the thickness of aquifer, soil structure, and interlaying manner of sublayers.Key words: land subsidence, groundwater, monitoring, GPS, Iran.
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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 | 0.000 | 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".