Modification of Detention Basin Outlet Structures using Calibrated SWMM Models
Bibliographic record
Abstract
Storm Water Management Model, SWMM, is a large, relatively complex software package capable of simulating the effects of runoff from sub-catchments through pipe/channel networks and storage facilities, andfi.nallytoreceivingwaters.This chapter descn"bes a study where detailed SWMM models were developed as an integral part of a study of the outlet structure and associated modifications.Different outlet structure configurations were compared, thereby resulting in an optimal configuration of the structure.The need was based on changes in the Metropolitan St. Louis Sewer District's (MSD) detention basin design requirements (Akan, 1989).It was previously required that the outlet structure controls the outflow of the 15-year storm to the same level as the pre-development discharge rate.It is now required that the outlet structure control the outflow of both the 2-year and 100-year outflows to the pre-development discharge rate or to the required basin-wide release rate determined by MSD (McEmoe, 1992).Another factor motivating modification of outlet structures is the observation by a number of interested individuals and communities of"little or no storage" of storm water occurring in these basins during storm events with subsequent downstream flooding.Detailed SWMM modeling of eight different basins in the St. Louis, Missouri, metropolitan area showed "little or no" reduction of the 2-year storm outflow, but significant reduction of the post-development 100-year storm outflow.Thus, the objective reached by modifying the existing outlet structures is to Morris, C.D. and J.P. Asunskis.2002."Modification of Detention Basin Outlet Structures using Calibrated SWMM Models."
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".