A Novel Approach to Aquifer Classification Using Hysteresis Loop Analysis and Deep Learning for Sustainable Groundwater Management
Bibliographic record
Abstract
Aquifer classification plays a pivotal role in understanding groundwater dynamics and informing sustainable water resource management, especially in regions under significant stress from over-extraction. This study presents a novel remote sensing-based methodology for classifying aquifers represented by monitoring wells within the study area. The approach integrates stress-strain analysis, incorporating deformation data derived from Interferometric Synthetic Aperture Radar (InSAR) and groundwater head measurements from monitoring wells, utilizing advanced deep-learning techniques. Groundwater data from piezometric wells are utilized to create image-based representations of hysteresis loops derived from stress-strain diagrams, capturing aquifer deformation under varying drawdown and recovery cycles. A convolutional neural network is applied to extract high-dimensional features characterizing aquifer response dynamics. Principal component analysis is then employed to reduce dimensionality, highlighting the most significant features driving classification. Finally, unsupervised clustering methods are used to group piezometric wells, revealing distinct aquifer types and deformation patterns. The proposed methodology is tested in three hydrologically and geologically diverse regions of Iran: Shabestar, Urmia, and Neyshabur Plains. In the Shabestar and Urmia Plains, located near the hypersaline Lake Urmia, intensive groundwater extraction has severely strained local hydrological and ecological systems, contributing to declining lake levels and increased stress on water resources. Similarly, in the Neyshabur Plain in northeastern Iran, characterized by its arid to semi-arid environment and intricate geological features, excessive groundwater use has led to significant aquifer depletion and land subsidence. The proposed approach effectively identifies different aquifer types, analyzes the balance between elastic and inelastic deformation, and determines aquifer responses to varying degrees of groundwater extraction. By integrating InSAR-based deformation monitoring of ground surface with advanced deep learning techniques, the study provides a comprehensive framework for aquifer system characterization. The findings are particularly valuable for regions with scarce geological and hydrological data, offering insights to guide sustainable groundwater management practices, mitigate environmental degradation, and support effective decision-making.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".