A hydrodynamic approach to address Yamuna riverbed development in Delhi
Bibliographic record
Abstract
Most critical flood problems occur in urban areas where values at risk are higher and damages tend to be heavier. In the present study, a hydrodynamic simulation of the river Yamuna has been generated under different designated flood flows to delineate the land availability under existing and modified riverbed geometry including channel dredging and riverbed dressing. Flood flows for various return periods, namely once in 10, 25, 50, and 100 years are estimated based on the past 41 years of recorded data using flood frequency analysis. The model set up considered the river stretch of 23 km from the Wazirabad barrage upstream to the Okhla barrage downstream, which includes manmade structures like bridges and barrages. Model calibration is achieved by comparing the water surface elevations with observed data at various downstream gauge stations. The land availability based on submergence scenarios under existing riverbed condition is estimated to be 1147.6, 873.1, and 762.6 ha, respectively, for 10, 50, and 100 year return flood. Various simulations are generated including channel dredging and riverbed dressing to obtain safe height and safe width for dressing the riverbed. The modified river geometry considered channel dredging in 750 to 1350 m width, and riverbed dressing for safe height and width in three levels in various sections. The land availability under this scenario is estimated to be 2197, 1129, and 545 ha for 10, 50, and 100 year return flood, respectively. It is proposed to dress the riverbed by utilizing the material obtained from channel dredging in three levels to maintain the transverse and longitudinal slope, river morphology, and river system. Developmental planning in the riverbed may include utilization of land at three levels for various purposes such as playgrounds, nursery, etc. However, it is suggested that the feasibility study of the project requires detailed engineering including slope stability prior to implementation of the riverbed development.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".