Multi-Objective Calibration of SWMM for Improved Simulation of the Hydrologic Regime
Bibliographic record
Abstract
This chapter presents a multi-objective calibration of the Storm Water Management Model (SWMM) using the Non-dominated Sorting Genetic Algorithm (NSGA-II) developed by Deb et al. (2001). The effects of model calibration on the representation of various hydrologic characteristics with ecologic and geomorphic relevance are studied. Results indicate that there are modeling conflicts between low flows, medium to bank-full flows, and high flows. As a consequence, calibration improvements of minimum water quality maintenance flows, decreases the agreement between computed and observed flows above bankfull elevations. The presence of these trade-offs should be acknowledged in model-based watershed management strategies in order to minimize the uncertainty bias towards certain characteristics of the flow regime. Results show the effects of such trade-offs in the model accuracy to represent different hydrologic quantities, such as mean monthly flows, peak monthly flow, minimum monthly flows, and flow durations and exceedance volumes for different flow ranges and months.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".