Shades of Green: Using SWMM LID Controls to Simulate Green Infrastructure
Bibliographic record
Abstract
Green infrastructure is increasingly being considered for application in urban storm water management designs.Many municipalities, regulatory agencies and advocacy groups promote the use of low impact development (LID) to reduce runoff and increase infiltration.To show LID benefits, engineers must quantify the advantages of green versus traditional grey infrastructure.The United States Environmental Protection Agency (USEPA) updated its Storm Water Management Model (SWMM) with explicit LID controls in 2009 to assist engineers in quantifying green infrastructure benefits (Gironas et al., 2009).SWMM can now simulate five LID devices: bioretention cells; infiltration trenches; porous pavements; rain barrels; and vegetated swales.Limited documentation is available regarding modeling techniques using the LID controls; the SWMM5 Applications Manual discusses modeling LID using SWMM features available before the LID controls were added.This study presents an initial evaluation of green infrastructure modeling using SWMM LID controls in SWMM version 5.0.022,released April 2011.The bioretention cell was selected for this evaluation because it most accurately represents a rain garden.Regardless of the LID control selected for simulation, the basic parameters for soil properties, storage volumes, surface characteristics, and underdrains are essentially the same among green infrastructure devices.A demonstration study from Madison, Wisconsin (Selbig and Balster, 2010) was used for comparison.In that study, the United States Geological
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".