Water distribution system failures : an integrated framework for prognostic and diagnostic analyses
Bibliographic record
Abstract
The main goal of this research is to develop an integrated decision support system framework for prognostic and diagnostic analyses of water distribution system (WDS) failures. The interventions based on the prognostic analysis will reduce the likelihood of failures, and in case of a failure, will minimize the consequences of the failures. The framework consists of five novel models. For prognostic analysis, the first model evaluates the reliability of WDS in terms of utility and belief of the estimated utility. This model provides a measure of degree of uncertainties in reliability estimation and helps to plan and design a reliable WDS. Based on various influencing factors, the leakage potential model evaluates potential for leakage under varying operating conditions. Based on identified symptoms of failure such as taste and odor, and the causes of failure such as free residual chlorine, the water quality failure potential model evaluates the potential for water quality failure (WQF). For diagnostic investigation, the leakage location and detection model, the fourth model, identifies the presence of an actual leakage, if any, and the most probable leakage location in WDS. The WQF detection model, the fifth model, identifies the most vulnerable location in the WDS and in case of failure, identifies the most probable reason and most probable source of water quality failure. Finally, based on the developed models and other external information, an integrated prognostic and diagnostic decision support system framework has been developed. The prognostic capabilities of the framework provide states of the WDS and evaluate failure potentials of the system. The diagnostic capabilities of the framework help to reduce false positive and false negative predictions, and identify the failure location with minimal time after the occurrence which minimizes the consequences of failure. The framework has ‘unique’ capacity to bring the modelling information (hydraulic and Quality), consumer complaints and laboratory test information under a single platform. The outcomes of this research widely addressed the uncertainties associated with WDS which improves the efficiency and effectiveness of diagnosis and prognosis analyses of WDS failures. The research also provides new insights on how to incorporate fuzzy set in the assessment of WDS failures. It is expected that the developed integrated framework will help municipalities to make informed decisions to increase the safely and the security of public health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".