Modeling Urban Sewers with Artificial Fractal Geometries
Bibliographic record
Abstract
Sewer models are used to simulate complex urban hydrology.However, the development of empirical models can be difficult given the limited availability of sewer plans and the time required to incorporate the system layout.In contrast, fractal geometries can be used to overcome some of these constraints.In this study, two highly impervious residential urban catchments (54 ha and 24 ha) serviced by a combined sewer in East Boston, Massachusetts are modeled using the Storm Water Management Model (SWMM).Two different modeling techniques are compared.The first is an empirical model using the physical characteristics of the network obtained from municipal sewer maps; the second is an abstract conceptual model incorporating fractal scaling laws often used to describe natural river basins.Both modeling approaches were calibrated with 1 month of empirical 5 min interval sewer flow measurements.The models predicted similar total discharge volumes and peak flows over the course of 10 observed rainfall events (0.5 mm to 12.7 mm).Model resolution was tested by simulating the 54 ha catchment as 1, 10, 24 and 173 subcatchments; accurate simulations could be produced for all of the resolutions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".