A multi-objective optimization framework to support integrated stormwater management
Bibliographic record
Abstract
This thesis presents a multi-objective optimization framework to support the application of models to integrated stormwater management processes. The framework includes three main stages, (i) multi-objective calibration of the hydrologic model, (ii) multi-objective optimization of stormwater best management practices (BMPs), and (iii) evaluation of selected BMP designs using additional calibration solutions. The benefits of the multi-objective optimization framework are illustrated by using two case studies. Results from the multi-objective calibration showed that calibration trade-offs may exist. Also, selection of a calibration solution to be applied as the evaluation tool is not a straightforward process, particularly when there is more than one objective that conflict among each other. Furthermore, the design of detention ponds at the watershed scale, using an approach that combines watershed-wide performance criteria, and standard design methods, was successfully implemented using the multi-objective optimization algorithm. Finally, it was shown that the evaluation of selected detention pond designs using alternative calibration solutions may render benefits in terms of minimizing unexpected system performance due to model uncertainty. Both the calibration and the design optimization are based on the evolutionary multi-objective optimization algorithm called Non-dominated Sorting Algorithm NSGA-II (Deb et al., 2002), and the Storm Water Management Model (SWMM).
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".