Rooftop Rainwater Control - Combining Storage Tank, Vegetation and Rooftop Retention
Bibliographic record
Abstract
Over the past few years, rapid urbanization, population growth and the projected climate change have contributed to global water scarcity and urban flooding. Thus, the rainwater harvesting system in buildings is getting popular for increasing water efficiency and reducing stormwater runoff. However, it is required to size the rainwater harvesting system adequately for improving its operation. An oversized system increases the capital cost, while an undersized system results in an unreliable water source. Therefore, the storage tank's sizing is the most critical objective for optimizing the overall system in a dense urban location. Rainwater harvesting system and the vegetated roof technology can be particularly promising to address the issue because the vegetated roof and blue roof perform very well as a stormwater management tool by providing reduced stormwater runoff generation. The research identified the factors involved in managing runoff from a complex set of rooftop arrangements, having partially vegetated and non-vegetated areas and developed a set of guidelines to optimize the rainwater harvesting system by utilizing vegetated roofs. The research analyzed four mid-rise buildings of the Ryerson University campus, and the results confirmed that an increase in the total percentage of vegetated area coverage reduces the rainwater storage tank size to a great extent. For a small institutional building, a 40% agricultural roof performs the best for meeting the outside non-potable water demand reducing the annual overflow from the tank. For 60% vegetated area coverage, the municipal service water must supplement the rainwater to meet the total water demand. In terms of substrate depth, 150mm yields the most reasonable benefit. Finally, a blue roof should be combined with a vegetated roof to maximize the stormwater retention and detention onsite.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".