Determining Peak Flow Recurrence in Combined Basins with Limited Flow Data Using Genetic Algorithm Calibration
Bibliographic record
Abstract
The City of Columbus, Ohio, completed a comprehensive Wet Weather Management Plan (WWMP) to mitigate hydraulic deficiencies in the City's main trunk sewers and to perform a Long Term Control Plan (LTCP) to address combined sewer overflows (CSO) to the Scioto and Olentangy Rivers. The recommended solution includes a deep tunnel that will capture combined sewage overflows from the downtown CSO regulators. This combined flow tunnel will ensure that peak flow from the downtown combined sewer basins is captured up to a specific peak flow recurrence level. Due to the lack of long-term flow meter and downtown rainfall data, it was difficult to estimate peak flows for selected recurrence levels. Therefore, the design team proposed a procedure where available two to three-years of quality-checked flow meter data and concurrent 15-min rain gauge data between the years 2000 and 2003 was used to calibrate a SWMM model using the PCSWMM Genetic Algorithm Calibration (GAC). The long-term hourly rainfall data, collected by the National Weather Service at Port Columbus International Airport, in conjunction with the calibrated SWMM 4.4h model was then used to generate 56 y of flow records from each combined basin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".