Model Calibration of a Large Urban Sewer System using Radar Precipitation Information
Bibliographic record
Abstract
This chapter describes the development and calibration of an XP SWMM Project Model of the City of Ottawa's major interceptor and collector sewers.The Project Model was developed to confirm the size and assist in the development of an operating strategy for the Somerset Wastewater Storage Facility (SWSF).Significant temporal and spatial vaTiability of rainfall over the large West Nepean sewershed area precluded the calibration of the entire Project Model utilizing precipitation information from the area rain gauges alone.Rainfall data was developed for a virtual rain gauge network, which provided 5-minute rainfall volume estimates for each 1 km 2 of the drainage area, utilizing data from the existing rain gauges and the weather radar infmmation.Final Project Model calibration was performed using the flow data collected at six flow monitoring stations in the West Nepean portion ofthe collection system and the virtual rain gauge data averaged over the Project Model sub-catchment areas.In general, measured and modelled peak flows and event volumes matched well, with 17 out of 24 events having peak flows within the 15% envelope.Measured and modelled event volumes were within a 15% envelope for all 24 model calibrations.Rainfall estimates for the large West Nepean sevlershed area based on radar imaging enabled the calibration of the Project Model.which would not have been possible using data obtained from the area rainfall gauges alone.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".