Development of a Multi-Scale Stormwater Management Modelling Methodology
Bibliographic record
Abstract
The evaluation process of the land development project becomes complicated, as the simulation results of Fine-Scale Models (FCM) of lands proposed for development cannot be easily transferred to the watershed Coarse-Scale Models (CSM) due to parameterization problem. In this research study, a Multi-Scale Modelling (MSM) methodology was developed to minimize the difficulty in transferring the result into the different spatial scales of the models. The developed MSM methodology was tested on Ganatsekiagon Creek’s sub-watershed, simulating several scenarios. The analyses of model results show that the CSM is generating a lower Peak Flow Rate (PFR) and Flow Volume (FV) than the MSM, while the increasing level of development with BMPs leads to a rise in FV and a decrease in PFR. The comparison results of with and without BMPs show that the stormwater management ponds are effective in PFR reduction and the implementation of LID practices can be effective in FV reduction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".