An Integrated Hydraulic and Hydrologic Modeling Approach for Roadside Bio-Retention Facilities
Bibliographic record
Abstract
Roadside bio-retention (RBR) facilities are low impact development practices, which control urban runoff primarily from road pavements. Using hydrologic models, such as the US EPA Storm Water Management Model (SWMM), RBR are typically designed with some fundamental assumptions, including where runoff completely enters the facilities and fully utilizes the whole surface area for percolation, detention, filtration, and infiltration to the surrounding soils. This paper highlights the importance of inlet hydraulics and the spatial distribution of inflow along a RBR, and proposes an integrated hydraulic and hydrologic modelling approach to simulate its overall runoff control performance. The integrated hydraulic/hydrologic modelling approach consists of three components: (1) A dual drainage hydrologic model to simulate runoff generation, runoff hydrographs entering and bypassing a storm inlet, and the outflow hydrograph from a fully utilized RBR; (2) a computational fluid dynamic model to determine the inflow distribution along a RBR; and (3) an overall runoff control performance analysis of RBR by considering the inlet efficiency, and the partially and fully utilized RBR during a storm event. A case study of an underground RBR in the City of Toronto was used to demonstrate the integrated modelling approach. It is concluded that; (1) inlet efficiency of a RBR will determine the overall runoff control performance; and (2) the inflow distribution will dictate the effective length of a RBR, which may affect the overall runoff control performance.
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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) | 0.001 | 0.001 |
| 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) | 0.002 | 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".