Modeling a Bioretention Basin and Vegetated Swale with a Trapezoidal Cross Section using SWMM LID Controls
Bibliographic record
Abstract
The Low Impact Development (LID) Control module is utilized in the United States Environmental Protection Agency’s Stormwater Management Model (USEPA SWMM) to predict the hydraulic performance of a variety of sustainable stormwater technologies. Data collected in 2019 from the monitoring of a pilot project in Montreal was used to verify the ability of the Bioretention LID Control (which assumes a rectangular cross-section) to accurately simulate outflow from a structure with a trapezoidal cross-section. Two types of LID facility were modeled: one releases captured inflow through a perforated underdrain below the soil layer (bioretention basin; BB); and the other is drained at the surface of the soil layer (vegetated swale; VS). Initially, the modeled LID structures were sized identically to the field surface areas. However, it was necessary to change their model representation to account for the non-rectangular shape of the soil layer. In addition, a sensitivity analysis was completed, and the most influential parameters were identified as the conductivity slope and seepage rate. Both the alteration of the LID structure representation and the parametric calibration greatly improved the simulated outflows from the vegetated swale resulting in an increase of the Nash–Sutcliffe efficiency (NSE) coefficient from −0.6 to 0.64 (NSE >0.5 is acceptable for hydrologic models according to the literature). The bioretention basin calibration did not prove as successful. The evaluated LID Control module presented better predictive capabilities for the basin with a simpler overall design (VS).
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".