SWAT Modeling to Assess Water Availability in the Command Area of the Gandak River Basin, India
Bibliographic record
Abstract
It is essential to analyse water availability in a sub-basin for the management of water resources. In this study, a sub-basin of the Gandak River, with an area of 973.54 km2, has been selected to study water availability using a SWAT model with remote sensing and GIS support. For the analysis, hydrological and meteorological data for the year 2002 to 2021 have been used in addition to the land-use/land cover maps, soil map, and slope map. The SWAT model was simulated throughout a twenty-one-year period (from 2002 to 2021). To determine the most important watershed parameters, a sensitivity analysis of the model was carried out. Calibration was performed using data from 2002 to 2014 and validated from 2015 to 2021. The following statistical parameters were measured: Coefficient of determination (R2), Root Mean Square Error (RMSE), Percent Bias (PBIAS), and Nash Sutcliffe Efficiency (NSE). The results obtained for these statistical parameters indicate that the model results are satisfactory.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 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".