Implementation in PCSWMM Using Genetic Algorithms for Auto Calibration and Design-Optimization
Why this work is in the frame
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Bibliographic record
Abstract
This paper discusses the development, application and performance evaluation of a genetic algorithm-based software tool (PCSWMM) for calibration of the Storm Water Management Model (SWMM version 4.4h). Model calibration is a crucial step in developing a useful storm water model, especially when the model is used to evaluate one or more "what-if" scenarios in an existing storm water system. While a SWMM model can be applied to very simple modeling problems, it can also be quite complex, containing thousands of significant hydraulic and hydrologic entities. As each model entity may contain as many as a dozen sensitive, uncertain parameters, and as the volume of available observed time series data increases, rigorous manual calibration can be expensive and time-consuming. For this reason, model calibration is often not performed, or performed inadequately. An automated calibration tool is described that significantly reduces the effort required for calibration and design optimization. A sample application is provided. Such tools encourage the adoption of more thorough model development and verification protocols, and better design.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it