Trend analysis and learning-based groundwater level modelling over a tropical river basin
Bibliographic record
Abstract
Groundwater trend analysis and modelling is challenging due to partially explicable factors and unexplained human influence. The Hurst index, sequential Mann–Kendall, and classical Mann–Kendall test offer a comprehensive groundwater trend analysis. A learning-based approach is developed to model groundwater levels using climatological variables of rainfall and temperature. Twenty-four locations were considered over Periyar river basin of Kerala, India, for the years 1996–2019, and during January, April, August, and November (JAAN) months. Significant trends were observed at 14 locations in at least one of the JAAN months, which is about 58%. Of these, eight locations exhibited positive trend, signalling a decline in groundwater supplies. The developed model yielded notable improvements in precision with 50%, 79%, 75%, and 83% of the locations in month-wise order. To gauge the model performance, observed and predicted location clusters obtained using k-means clustering are juxtaposed for the years 2017–2019, on both individual and average basis. This assessment indicated only one well transitioning in August, with the average approach resulting in a closer match to the original clustering for most of the wells. These findings will benefit future stakeholders and policymakers in optimising resource management strategies over the basin and wider.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".