Urban Resilience : The Optimization of Sustainable Urban Stormwater Management
Bibliographic record
Abstract
With increasing urbanization and intensifying climate change impacts, an increasing amount of impermeable surfaces can result in surface stormwater runoff that overwhelms existing stormwater drainage systems. In response to the urgent need for sustainable cities, there has been a growing body of research on an increasingly popular urban planning initiative: sustainable urban stormwater management or ‘low impact development’. In this work, we expand on this discussion by investigating the stormwater resilience of the University of British Columbia’s Vancouver campus and the effectiveness of low impact development practices in increasing the resilience to climate change impacts. Specifically, the report examines various low impact development controls, including green roofs, rain gardens, and permeable pavements, evaluating their performance in managing stormwater runoff and reducing flood risk. The study used a rainfall-runoff analysis, employing a stormwater management model (SWMM 5.2.1) to simulate projected storm events under a moderate climate change scenario. The research findings indicate that a combination of permeable pavements and rain gardens are the most effective low impact development controls to enhance stormwater resilience. This supports existing research that low impact development can increase the infiltration of stormwater into the ground, thus reducing the volume of stormwater runoff and subsequent flooding events. This study concludes that low impact development is a promising urban planning initiative to enhance urban resilience to climate change impacts, and emphasizes the need for further research to optimize the design and implementation of low impact development controls. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".