Rainfall Runoff and Flood Plain Inundation Modeling of the Kharkai River, India using HEC-HMS and HEC-RAS
Bibliographic record
Abstract
A rainfall-runoff model using Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) was carried out for the Kharkai River of Subarnarekha Basin, India to determine the magnitude of runoff from a known quantity of rainfall and the terrain inundation. In the present study, for the calibration and validation, the rainfall events of October 1–20, 2017 and July 12–31, 2019 were used. In the process of calibration, the computed discharge was found to be 1,674 cumecs against the observed discharge of 1,820 cumecs, with a Nash-Sutcliffe efficiency of 91.78%. Whereas in the process of validation, the computed discharge was found to be 3,266 cumecs against the observed discharge of 3,589 cumecs, and a Nash-Sutcliffe efficiency of 91.02%. Furthermore, to generate various inundation maps of the Kharkai River, an SRTM-DEM of 30 m x 30 m resolution, and a 30 m resolution DEM was used in the simulation process. The results of the inundation study showed that the 5, 20, 50, 100, and 500-year return period floods determined using Gumbel’s method are 3,647.791 m3/s, 5,797.717 m3/s, 7,160.188 m3/s, 8,181.168 m3/s, and 10,540.5 m3/s, respectively, and the corresponding inundation areas were 20.98 km2, 33.65 km2, 39.89 km2, 44.07 km2, and 56.65 km2, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".