A novel way of identifying agricultural water drainage systems and their impact on catchment hydrology
Bibliographic record
Abstract
Water drained from agricultural lands is getting more attention as its valuable water is lost from groundwater storage. The historical location of buried agricultural water drainage systems is not known very well. Hence, first, finding the location of those infrastructures is critical. Several methods have been applied in the past, including decision tree classification (DTC), remote sensing based, using radar, etc. However, all the methods neglect the primary cause of the drain application, which is groundwater. Hence, a novel approach is introduced that considers groundwater in the identification procedure. We used two case studies for drain identification, one from Ontario, Canada, and another from Belgium. Furthermore, a physically based and fully distributed modeling approach (SWAT+gwflow) is conducted to investigate the impact of these drainage systems in the catchment hydrology of the Kleine Nete watershed, Belgium.The result of the drainage system identification has indicated the pitfalls of the already existing methods where accuracy as low as 17% was recorded. On the other hand, the additional filtering based on groundwater head enables us to find an additional 19.4 km2 area. Therefore, the use of groundwater level as an additional filtering technique is vital for increasing the accuracy of tile drain/ditch network identification. Next, drains have been shown to affect hydrology, where a 15% decrease in groundwater evapotranspiration, a 50% reduction in groundwater saturation excess flow, and a 39% decline in groundwater discharge to streams are observed.
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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 | 0.000 |
| 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".