Beyond the Pipes: Performance Management of Water Supply Systems under Uncertainty
Bibliographic record
Abstract
Performance evaluation of water supply systems is essential for asset management decision-making. Most studies focus on the infrastructure systems’ performance indicators without linking them to levels of service (LOS). Although some attempted this connection, they often overlooked different infrastructure systems, raising concerns about the comprehensiveness of the performance evaluation process. The present study, therefore, developed a LOS-oriented performance evaluation framework for potable water infrastructure systems. This framework considers system-level and non-system-level performance indicators, providing a holistic and comprehensive infrastructure assessment. This framework offers a holistic performance evaluation of potable water infrastructure across 10 LOS dimensions. A five-level performance scale was established for the performance indicators within the framework. In order to address the inherent uncertainty of operational data, a fuzzy synthetic evaluation (FSE) analytical strategy was utilized for the computations. The developed framework and FSE analytical strategy were demonstrated using case study data from a municipality in Ontario, Canada. The case study results indicated that the LOS-oriented standardized infrastructure performance varied between 0.74 and 0.92 (out of 1) across the four scenarios examined. The sensitivity analysis revealed the carbon footprint of water treatment facility operations, customer feedback, response time of unplanned interruptions, operational efficiency, field accidents, and service availability as the critical performance indicators.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".