Exploration of Potential Groundwater Zones in Nambiyar Watershed, South India using Frequency Ratio and Multi-Influencing Models
Bibliographic record
Abstract
Groundwater potential is dynamic and fluctuates with respect to draft and recharge. The purpose of this study is to investigate the potential groundwater zones of the Nambiyar watershed in South India utilizing a probability-based bivariate statistical model frequency ratio (FR) and multi-influencing factor approaches (MIF). For this, spatial relationships between ten factors viz. slope, rainfall, lineament and drainage density, geology, geomorphology, soil texture, land use/land cover, well density, topographic wetness index, and groundwater occurrence were assessed. A total of 162 wells were selected for the study, of which 60% (97 dug wells) were used for training the model, and the remaining 40% (65 dug wells) were used for validating the results. The potential groundwater zones were classified into five categories: very low, low, moderate, high, and very high. Very high zones classified using the FR and MIF models are 186 km2 (28.03%) and 97.84 km2 (14%), respectively, whereas very low category areas are 63.29 km2 (9.50%) and 64.02 km2 (9.61%) of the watershed. The results were validated using well data by generating the AUC (area under the curve). The validated results revealed that the AUC for the frequency ratio model was 72%, while the MIF was 62%. This study explores potential groundwater zones using GIS and remote sensing techniques, benefiting government agencies and private sectors for better resource management.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".