Field Evaluation of Discretized Model Setups for the Storm Water Management Model
Bibliographic record
Abstract
The Stormwater Management Model (SWMM) is a hydrologic-hydraulic model often used to simulate water flows in urban drainage systems and changes in water quality.The unsteady flow hydraulic solver in SWMM solves mass and momentum conservation equations for the entire conduit length, and mass is conserved at each junction.This link-node approach used by SWMM does not allow for discretization (i.e.intermediate calculation points) between consecutive junctions, which is adequate in gradual filling scenarios with appropriate calibration and suitable selection of routing time steps.However, because there are more rapid filling scenarios that are associated with intense rain events, the link-node solution approach will affect the accuracy of the hydraulic calculations.This work presents the results of a field investigation in which predetermined volumes of water were suddenly released into a physical stormwater collection system.Level loggers were installed to measure flow depth and outflow rates in these tests.The results were compared with SWMM modeling results obtained by the link-node approach using alternative SWMM model setups which included additional intermediate discretization.The goal was to assess the potential benefits of SWMM discretization in the context of rapidly filling collection systems.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".