Municipal Water Pipeline Leak Detection System
Bibliographic record
Abstract
This work explores the feasibility of modernizing the current leak detection method used by the City of Kitchener which involves manual acoustic readings performed on a third of the city annually. We seek to develop software to detect leaks in real time using pressure and flowrate data collected by remote sensors in water pipelines. The primary objective is to update the detection to be in real time and increase sensitivity in the process by detecting smaller leaks that could have previously gone undetected. We have decided to achieve this using a time-series classification algorithm: MLSTM-FCN and the LeakDB dataset to represent a scaled-down version of the water distribution network in the City of Kitchener. The configuration of using pressure sensors only was selected from the results of the reduced feature test. It provided satisfactory performances in the proceeding generalization and localization tests. The solution fulfills all constraints and criteria. Based on the analysis, it is recommended to install 388 pressure sensors in the City of Kitchener as it minimizes the cost, without sacrificing the accuracy of the model.
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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 | 0.001 |
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".