Complex versus Simple Stormwater Network Models - Modeling of the Keysers River Catchment
Bibliographic record
Abstract
In accordance with their objective of ensuring adequate stormwater drainage and the reduction of flood risk within the Cape Metropolitan Area, the Catchment, Stormwater and River Management Branch of the City of Cape Town commissioned a study to determine floodlines adjacent to the lower reaches of the Keysers River in the South Peninsula.Information relating to the hydrological characteristics of the catchment, as well as the physical stormwater network, was collected and incorporated into a complex model of the catchment When runoff from a recorded rainfall event was routed through the model, the simulation output was found to be significantly different from the observed catchment response.A simplified model, which incorporated the overall physical characteristics and average hydrological parameters of the catchment, was run using the same rainfall event In contrast to the results from the complex model, the output from the simple model showed a close match with the observed data.By reducing the time of concentration in the simple model, it was possible to produce a similar output to that of the complex model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".