An integrated InSAR-numerical approach for accurate groundwater head prediction
Bibliographic record
Abstract
In recent years, the decline of groundwater resources in productive aquifers within arid and semi-arid regions has become a growing concern. This depletion leads to lasting changes in aquifer properties due to fine particle rearrangement caused by pore pressure decline. The issue is especially critical in regions lacking reliable field data. Thus, it is crucial to develop methods that are less dependent on in-situ data but still offer reliable tools for monitoring groundwater levels in such regions. This study presents the deformation-driven groundwater head estimation model, primarily developed to estimate storativity by jointly analyzing groundwater head and Interferometric Synthetic Aperture Radar (InSAR)-derived surface deformation, separating seasonal and long-term components. The model then uses these estimated storage coefficients to simulate and predict groundwater levels without requiring detailed knowledge of aquifer type or properties. It can reconstruct historical head data and predict future levels from deformation alone. Model performance was evaluated across two distinct aquifer systems with diverse hydrogeological properties and deformation behaviors. Four wells in each aquifer ensured spatial representativeness. The model showed strong agreement with observed groundwater head, achieving average R 2 values of 0.70 and 0.94 in the simulation phase, and 0.65 and 0.34 in the prediction phase for the two aquifers, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".