Fuzzy cognitive maps for decision support to maintain water quality in ageing water mains
Bibliographic record
Abstract
The prioritization of water mains for renewal requires the simultaneous consideration of their structural integrity and hydraulic performance as well as their contributions to the deterioration of water quality. Presently, several decision models exist for water main renewal. Most consider the structural integrity of the pipes as the sole decision criterion, although some consider also their hydraulic capacity and others consider only network reliability. Various attempts have been made by water utilities to consider multiple decision drivers in their prioritization process, however, these are done through simple point scoring methods, which are essentially qualitative, inherently subjective and do not consider input uncertainties. Theimpact of deteriorating pipes on water quality in the distribution network has not been considered in decision process. This paper outlines a framework for assessing risk associated with water quality failures indistribution networks due to ageing mains. The available field data are both quantitative and qualitative, and when available, they are often uncertain and vague. Numerous factors affect water quality in the distribution system and the interactions amongst them are complex and often not well understood. For these and other reasons, fuzzy cognitive maps are examined as a decision support tool to develop an integrated approach for the renewal of water mains.
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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 | 0.002 | 0.005 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".