Utilizing GIS in the development of detailed distribution urban drainage models
Bibliographic record
Abstract
Stormwater management practices can mitigate the undesirable impacts of urbanization. Urban drainage models can play a significant role in comprehensive evaluation of stormwater management systems. This thesis presents a methodology for the development of a detailed distributed urban drainage model using Geographic Information System (GIS) databases. The approach incorporates delineation of spatial variables (subcatchment outlets, width, slope etc.) and other model inputs from digital data and assigns them to the GIS database by executing developed Python 2.7 scripts. The proposed methodology is applied on a case study area in Mississauga, Ontario. Observed rainfall data are used as model input and simulation is performed using PCSWMM 5.1.1279. The model performance is evaluated by comparing the uncalibrated simulated discharge with the observed one. The simulated hydrographs are of good fit with the observed hydrographs. The evaluation criteria justify the use of extracted and assumed parameters and the reliability of the developed methodology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".