Regional Hydrological Modeling of City of Tshwane Municipality using VisualSWMM
Bibliographic record
Abstract
In order to develop an integrated catchment management plan for present and future development scenarios, the City ofTshwane Metropolitan Municipality {CTMM), situated in Gauteng province, South Africa, required integrated data on runoff peaks and volumes along all its major watercourses amounting to approximately 1400 km.This is focussed towards improving the planning and management of stormwater drainage systems in all catchments within its area of jurisdiction.CTMM thus commissioned SRK Consulting (Pty) Ltd to compile a regional hydrological model of all its major watercourses as well as a river referencing system (RRS) and stormwater management information system (SMIS) to aid in managing the data.The main objectives of the project can be summarised as follows:• review all available data (rainfall, runoff, catchment, landuse); • define the locality and nature of existing major drainage systems;• establish integrated intensity-duration-frequency relationships;• establish integrated design runoff hydrographs along major drainage systems and watercourses; • establish a RRS giving digital and visual information of all existing floodline data, scanned images, spatial and numeric data; and • compile a SMIS for easy access and queries of the data.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".