Evaluating the impact of subsurface hydraulic barriers on Qanat flow rates using quantile regression forest
Bibliographic record
Abstract
Qanats, as hydraulic innovations, enabled the sustainable extraction and distribution of groundwater for irrigation and domestic use during history. This study presents a data-driven modeling framework that implements Quantile Regression Forest (QRF), Random Forest (RF), and Support Vector Regression (SVR) to predict Qanat discharge under altered subsurface conditions. Using field data from the Dirsak Qanat in northern Iran, a traditional drainage system recently enhanced by the construction of a subsurface dam (SD), we investigate the dam's effect on discharge potential. The modeling framework incorporates determination of multiple hydro-meteorological inputs including precipitation, temperature, evaporation, humidity, runoff depth, infiltration depth and groundwater levels observed at three monitoring wells. A binary (dummy) variable was also introduced to represent the presence or absence of the SD, thereby capturing the associated changes in boundary conditions. The analysis further revealed that the SD and evaporation are the most influential factors, highlighting the combined effects of anthropogenic modifications and climatic variations on the discharge behavior of the Qanats. It was also concluded that the QRF model with a Nash-Sutcliffe Efficiency (NSE) of 0.818, demonstrate strong predictive capability in capturing complex and non-linear hydrological interactions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".