Improving Nonlinear Optimization Algorithms for BMP Implementation in a Combined Sewer System
Bibliographic record
Abstract
Implementing best management practices (BMP) on watersheds could help mitigate the effects of urbanization and climate change on the hydrological cycle. Techniques such as retention ponds, rain gardens, infiltration trenches, and green roofs vary in technical performance, space requirements, and cost. The trade-offs between these present a challenge toward BMP selection and placement, therefore requiring optimization. Three optimization methods were applied for BMP implementation on a combined sewer: linear programming (LP); genetic algorithm (GA); and simulated annealing (SA). LP served as a reference point. The SA solution was only marginally better, 4.7% cheaper, whereas GA’s solution was 17.9% more expensive after computations froze at a local minimum; both methods required approximately 18 h of computational time. A second round of optimization used the solution from LP as a starting point. This modification significantly increased the performance of GA, providing a new solution that was 14% cheaper than LP, with reduced computational times for both GA and SA. SA’s solution, though still cheaper than that of LP, was 3.9% more expensive than the one previously obtained with SA.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
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".