On‐Line Warning System for Pipe Burst Using Bayesian Dynamic Linear Models
Bibliographic record
Abstract
Abstract Pipe breaks are a recurrent problem in water distribution networks and detecting them quickly is crucial to minimize the economic and environmental costs for municipalities. This study presents a burst detection methodology applying Bayesian dynamic linear models (DLMs) on water flow time series combined with an outlier monitoring tool. The model is used to characterize the actual flow and, for each time, a one‐step ahead forecast distribution is obtained recursively before moving onto the next observation. The outlier detection method consists of comparing the main model with an alternative one wherein the mean flow is shifted to a higher value (as bursts tend to increase flow) to evaluate which model best fit the observed data. If the alternative model is favored, a burst alarm is issued. To verify the performance of this approach, the DLM and monitoring tool were applied on 2 yr of flow data from two district meter areas (DMAs) in Halifax (Canada), and a historical break data set is used to assess model accuracy. The model was able to detect up to 75% and 71.2% of the pipe breaks, with a false alarm rate of 5.15% and 12% in the first and second DMA, respectively. Finally, the proposed model allows for straightforward interpretation of model parameters, nonlinear relationship between flow and predictors of interest, naturally describes the uncertainty for future predictions, can easily accommodate missing values and can be tuned to maximize break detection or minimize false alarm rates to adapt to specific objectives of water infrastructure managers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".