How Much Is "Your Un-Calibrated Model" Costing Your Utility? Ten Lessons Learned from Calibrating the CRD Water Model
Bibliographic record
Abstract
Measured field data are required to calibrate a hydraulic model. Field data is typically collected through a hydrant testing program — the process of opening a hydrant and measuring the flow from it, while recording residual pressures at other hydrants in the area. This is followed by a desk exercise during which adjustments are made to the parameter values used in modeling the water system until a satisfactory match is obtained between modeled and observed values. The selection of hydrant test locations in a water distribution system is to ensure that effective data for model calibration is collected. For a given number of hydrant tests the objective is to maximize the calibration accuracy, i.e. the model's ability to reproduce the observed data. A total of 35 hydrant tests were completed in August 2009 by CRD staff. 306 hydrant data measurements were analyzed and used to calibrate the water model. Calibration agreements were within 2.5%. The hydraulic modeling predictions agreed with the values observed in the field within the AWWA guidelines on model calibration tolerances. The new calibrated model is being used by CRD as a practical tool for infrastructure planning, operational review, and fire flow and water quality analyses. Ten lessons learned from this project were developed and will be discussed in this paper. These lessons are meant to improve the model development and calibration process by reducing the amount of effort required to develop and calibrate the model, while increasing the accuracy and confidence in the model.
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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 | 0.008 | 0.031 |
| 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 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".