A Systematic Exploration of Uncertainty and Convergence of Inverse Transient Calibration for WDSs
Bibliographic record
Abstract
Despite over ten years of research into ITC techniques for water distribution systems, many problems remain. One reason for these difficulties is that real water distribution systems invariably have many other uncertainties in addition to the leakage rates and friction factors that are conventionally considered as unknowns. For example, properties such as pipe diameter, wave speed, the possible presence of air, the value of the water demand at the time of the tests, and uncertain measurement accuracy, all add to the complexity and difficulty of obtaining a reliable calibration. The current paper investigates quantitatively how several of these uncertainties deteriorate system calibration, and thus the paper generally considers the necessity of a systematic calibration approach to explicitly include these additional uncertainties during the ITC process. To this end, two evolutionary optimizations, namely Genetic Algorithms and Particle Swarm Optimization, are compared and contrasted during the ITC iterations. The advantage of the evolutionary algorithms is that they help the search to escape from poor local optima in multifaceted and complicate problems and thus to locate a good global (or near-global) optimum. However, even these approaches can often be expected to converge poorly when the full scale of the field problem is reflected in the search space.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".