Automated Detection and Location of Leaks in Water Mains Using Infrared Photography
Bibliographic record
Abstract
Leakage of water distribution networks is the most common reason of undesirable losses of potable water. Problems associated with water main leaks pose growing concern around the globe. These problems include water and energy loss, in addition to the risk it poses to structural damage of adjacent properties. In current practice, not all water leaks can be detected in a timely and cost effective manner. This paper presents a study conducted for detection of water leaks in underground pipelines, and identification of their respective locations using thermography infrared camera. The paper describes the field work and the experimental protocol which were carried out over 2 years in three different locations in greater Montréal (Canada) area in order to investigate factors that affect the applicability and limitations of using IR camera in water leak detection. These factors beyond those studied in previous work carried out by American Water Works Association and National Research Council. The paper presents a model developed to determine approximate location of leaks in water mains. The developed model was then applied successfully to detect and locate leaks in water mains in fall and spring seasons. It failed, however, to detect leaks in the summer and winter due to high pavement temperature and the snow coverage, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".