Inverse Optimization based Detection of Leaks from Simulated Pressure in Water Networks, Part 2: Analysis for Two Leaks
Bibliographic record
Abstract
Water networks lose significant volumes of water from the distribution network.This is because most water networks, especially in developing countries, do not have the techniques, equipment or monitoring systems required to enable the detection of leaks.An optimization-based approach is used to model leakage detection in water networks from simulated network pressures.This study explores the ability of the proposed model to simultaneously detect two leaks within the water distribution network and to determine the number of reference points required for the leaks to be detected.By changing the emitter property in the network hydraulic model, reference and simulated pressures are generated.Two nodes are injected with leaks of known magnitude and the model attempts to find the two reference nodes.Similarly, several simulated references are generated from stochastically simulated leaks.The reference and simulated pressures are compared with respect to selected observation or reference points within the network.The model detects leaks using the optimization functions for which the sum of squared errors (SSE) is equal to zero.For the range of leak sizes and the two scenarios considered, the model performs poorly (13%) in the Same Vicinity scenario and just below average (41%) in the leaks occurring in the Far Apart scenario.This may be due to the water network configuration affect-ing the sensitivity of the model to the leak sizes being considered.Relatively smaller leak sizes and various network configurations will be further investigated.Additionally, it was determined that a minimum of eight observation points is required for leaks to be detected.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".