Quantitative Hydrological Analysis Of West Banas River Basin, India
Bibliographic record
Abstract
Detailed hydrological analysis is carried out for the estimation of peak discharges at various locations of the basin and to assess the impact of factors that are driving the temporal change.West Banas basin has experienced frequent flooding with increased magnitude in last two decades.The goal of study is to accurately assess the response of catchment to the extent possible by developing different SWAT models specifically for monsoon months and the flooding years.Extraction of the basin and sub-basins, stream network is carried out on GIS platform.The entire West Banas River basin has been subdivided into 23 sub-basins.SWAT model is developed using rainfall and discharge data of 32 years The calibration results reveal a good performance of the model in streamflow simulation as indicated by the values of performance evaluation indices-R 2 , NSE and PBIAS.Different hydrological models are developed based on calibration of model using seasonal data such as monsoon and non-monsoon months as well as considering only flood, moderate or drought years.The model performance for each type of calibration scheme is analyzed and compared to determine optimum approach of seasonal calibration.The model developed for monsoon months produced better output in comparison to the models developed for all season months and non-monsoon months.Similarly, the model developed for flood years months gave better results in comparison to the models developed for moderate flood years and drought years.It is concluded that better flood analysis calibration must be carried out only using monsoon data.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".