Insights into green roof modeling using SWMM LID controls for detention-based designs
Bibliographic record
Abstract
Rainfall–runoff responses were observed in a laboratory environment using a rainfall simulator and a 7.43 m2 green roof cassette equipped with weighing lysimeters. SWMM LID controls were developed for various green roof profile configurations based on the physical properties of the composite materials. Unknown parameters affecting the drainage layer were adjusted in calibration. The cassette was modeled both as a typical Green Roof LID control using Manning’s equation at the drainage layer and a Bioretention LID control using an orifice equation in the drainage layer. Key parameters from a sensitivity analysis that were not directly measured were Manning’s roughness of the drainage layer, the drainage coefficient at the orifice, and the conductivity slope (HCO). The hydraulics of roof drains were considered by varying the width of the drain outlet from 0.25 m–1.22 m. During calibration and validation of multiple events, SWMM modeling resulted in a good fit compared to observed results (Nash–Sutcliffe model efficiency coefficient values of 0.70–0.89). Key limitations of SWMM green roof modeling are discussed with suggested improvements for future consideration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".