Efficiency enhancement of leakage detection and localization methods using leakage gradient and most affected sensors
Bibliographic record
Abstract
Leaks cause substantial economic and water losses for water distribution networks (WDNs). Thus, it is imperative to detect and localize leaks. This paper proposes an efficient set of methods for detecting and localizing leaks, with three main steps: model calibration, leak detection, and leak localization. First, demands, pipe diameter, and pipe roughness in the hydraulic model are calibrated with a genetic algorithm (GA). The X-bar method and cumulative sum control chart are then implemented to detect pipe bursts and incipient leaks, respectively. Lastly, a reduced search space is defined and searched with a GA to locate leaks. The proposed strategy was tested on the hypothetical WDN of L-Town from the Battle of the Leakage Detection and Isolation Methods. Results show that 12 of 19 leaks were detected, and 8 of the 12 leakages were accurately located. Compared with other approaches, the proposed approach is more efficient and equally effective.
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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 | 0.001 | 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".