A Two Stage Optimization Approach for Calibrating Water Distribution Systems
Bibliographic record
Abstract
BWCN is a competition for calibrating pipe roughness coefficients and demand pattern multipliers of C-Town Water Distribution System (WDS) to measured SCADA (hourly tank levels and pump flows) and fire flow test data in a 1-week operation. In a pre-calibration step, quality of the data is assessed, base demands for the fire flow tests are estimated, by mass balance, and pipes are grouped and their nominal values and variation range are determined. In this study, the calibration problem is solved in two stages, each of which tunes a portion of decision variables (DVs) that significantly impact the corresponding objectives while other DVs are set to their nominal (or calibrated) values. Dynamically Dimensioned Search based optimization algorithms are used in both stages because the default algorithm parameter setting is robust. Stage-1 aims to fit the fire flow test measurements that are highly affected by pipe roughness coefficients. Also, demand pattern multipliers for hour-1 SCADA must be calibrated in this stage because the base demand during the fire flow tests is roughly the same as those in hour 1. Ideally, a single solution must minimize the calibration error for all the measurements simultaneously. However, since no perfect model and/or data set exist, objective functions (error metrics) are usually in conflict. Therefore, this stage is set up as a bi-objective optimization problem to minimize the calibration error in simulating fire flow test measurements versus simulating hour-1 SCADA measurements. At the end of this stage, multi-criteria decision making is utilized to select candidate solutions to be evaluated in stage-2. In stage-2 demand pattern multipliers are calibrated to fit the SCADA (tank levels and pump flows). The WDS model performance in each hour is independent from subsequent hours; therefore, stage-2 is set up to calibrate demand pattern multipliers hour by hour starting from the hour 2 to 168 (1 week). All candidate solutions from stage-1 are evaluated in stage-2 and one of them is selected as the final solution to the C-Town calibration problem based on multi-criteria decision making. On average, the final calibrated model estimates static pressure, fire flow tests, tank levels and pumping flow rates of SCADA to within 3.5%, 1.5%, 1.0%, and 2.5% of the measured data respectively.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".