Assessing the Performance of HEC-HMS and SWMM Models for Rainfall–Runoff Simulation for Urban Watershed
Bibliographic record
Abstract
Understanding the hydrological response of a watershed to rainfall involves comprehending how it translates rainfall into runoff, considering infiltration, surface flow, and groundwater contributions. Simulations of runoff–rainfall are crucial in managing water resources, environmental protection, and infrastructure developments. Several hydrologic models exist from which simulations of runoff–rainfall may be made, but there is a need for an appropriate model that best fits the catchment in question. This research will focus on simulations of the urban watershed’s runoff using two hydrologic models: HEC-HMS and SWMM. The study area selected is Briar Creek Watershed in Charlotte, North Carolina. The hydrological and meteorological data obtained from the USGS are preprocessed using ArcGIS to provide input for these hydrological models. The performances of HEC-HMS and SWMM models against observed streamflow data are compared in both calibration and testing phases. The reliability of the concerned models is assessed through three performance metrics: NSE, PBIAS, and RSR.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".