Hydrological efficiency evaluation tool of urban stormwater best management practices
Bibliographic record
Abstract
The Brussels Environment Agency designed a decision-support tool (QUADEAU) for the sustainable management of stormwater in urban and developed areas.The tool aims to evaluate and compare alternative scenarios for reducing water runoff in public spaces inside any neighbourhood.QUADEAU is an easy to use tool for urban designers and watershed practitioners allowing the evaluation of the hydrological efficiency of any public space in a renovation or a new urban project designed with best management practices (BMP).It is a tool thought out to provide processes of optimization and selection of BMP to meet the hydrological objectives and the program needs of the public space project.This paper describes how QUADEAU is setup and how BMP are evaluated inside the tool.Any public space project is modelled in the tool by giving specific characteristics of all areas of the project.Connections between surfaces of the project are introduced within the tool by creating flow networks.BMPs are selected according to site constraints and program requirements of the urban project before being designed by giving values to specific parameters for each BMP selected.The hydrological efficiency of the project, i.e. the amount of water that does not reach the outlet because it has been managed earlier in BMP, is evaluated during and after the design rain event.The tool gives the user the opportunity to optimize his project if the hydrological efficiency does not match the hydrological thresholds determined in function of the imperviousness of the neighbourhood, the type of project and the local hydrology.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".