A Non-linear Differentiable Model for Stormwater-based Irrigation of a Green Roof in Toronto
Bibliographic record
Abstract
Green infrastructure has potential to alleviate the environmental impact of rapidly growing cities. This potential has inspired laws in Toronto that require the inclusion of rooftops with large vegetation beds, called green roofs, into sufficiently sized construction projects. We study the problem of reusing stormwater to irrigate a green roof in Toronto, where potable water is the current irrigation source. The vision is that widespread reuse of stormwater runoff for irrigation of green roofs and other purposes can reduce sewer overflow volumes without over-building (with the added benefit of conserving potable water). Towards this vision, our goal is to develop and evaluate two pump controllers for transporting stormwater to the green roof of interest in simulation. A key contribution is our development of a site-specific non-linear model for stormwater flow using smoothing techniques that permits linearization and a standard model predictive controller (MPC). We compare the efficacy of the MPC, which anticipates the weather, and an on/off controller, which is reactive rather than anticipative, for the site in simulation. With further study, we are hopeful that this research will advance control systems technology to improve the performance of green and stormwater infrastructure in growing urban areas.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".