Bibliographic record
Abstract
The Storm Water Management Model (SWMM) is widely-used to evaluate, analyze and manage problems in both hydraulics and hydrology.In order to improve the reliability of the model, a parameter-optimization approach is required to determine the ''best" input parameter sets.Within SWMM, the hydrology module RUNOFF is the best candidate module for uncertainty reduction by parameter optimization.In this chapter we describe how the genetic algorithm (GA) method was developed to optimize SWMM RUNOFF parameters.The calibration method and its accuracy, efficiency, robustness and reliability are demonstrated.The basic principle of the GA is the same principle that controls the genetic reproduction process with crossover and mutation as the major operations.By applying the genetic algorithm to SWMM with the aid of the sensitivity wizard in the graphical decision support systemPCSWMM, a sensitivity-based method for automating the calibration of runoff model was developed.Overall, the average accuracy of the calibrated model was within 97% of the target dataset (TD) after approximately 58 cycles of GA calibration program, on the average.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".