Multi-criteria optimisation of the Muskingum flood model: a new approach
Bibliographic record
Abstract
Existing Muskingum hydrological routing models adopt a single criterion in the calibration process. Some models minimise the sum of the squared deviations between estimated and observed outflows (outflow criterion), while others minimise the sum of the squared deviations between the estimated and observed storages (storage criterion). However, models that adopt the outflow (storage) criterion result in a poor fit to the observed storages (outflows). This paper presents a new approach that incorporates both criteria in the calibration process and aids trade-off analysis. The multi-criteria function is expressed as a weighted function of normalised outflow and storage criteria, representing the deviations from ideal outflow and storage values. The routing procedure is based on the author's four-parameter Muskingum model with constant parameters and the fourth-order Runge–Kutta method. The proposed model was applied to three examples. A criterion weight of 0·4–0·6 was found to produce an excellent trade-off between outflow and storage criteria. The results show that the model substantially improves on both criteria compared with single-criterion models. The proposed model, which properly captures the entire flood propagating characteristics in calibration, should be of interest to hydrological engineers and practitioners.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".