A Genetic Algorithm for the Minimum Cost Design of a Stormwater System
Bibliographic record
Abstract
Optimization methodology for design of stormwater systems is developed.The methodology uses a Genetic Algorithm Cost Minimization tool (GA-CM) to evaluate stormwater drainage system project costs.Also used are design capacity and water quality controls, real-world design standards, cost analysis, PCSWMM and version 4.4HGUX of the US-EPA SWMM program.It was successfully applied to a realistic but hypothetical stormwater system to select a near-optimal (minimum cost) set of design parameters.The GA-CM considered standard design practices from (i) the Ministry of the Environment of Ontario 2003 Stormwater Management Practices Planning and Design Manual and (ii) design information collected from interviews with consultants.The detail provided in the GA-CM is perhaps beyond what consultants feel that they need today.Interviews with consultants emphasized the need to address sizing of significant design parameters (e.g.depth of storage facility).This was the focus of the optimization methodology developed.Genetic algorithm routines are a powerful tool for the selection of the best combination of stormwater system design parameters.Semi-automatic optimization of urban drainage systems and associated costs will lead to improved urban drainage design practices and improve stormwater quality discharges to downstream receiving waters.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".