Assessment of Unaccounted-for Water in Municipal Water Networks Using GIS and Modeling
Bibliographic record
Abstract
This chapter presents a study to calculate "Unaccounted-for Water" (UFW) due to high pressure in water networks.Unaccounted-for Water (UFW) including the physical and non-physical loss of water in the network is one of the parameters that managers should consider when considering the crisis of water supply in cities and the need to improve network efficiency.Increasing the number of consumers at the same time as increasing limitations of water resources requires network optimization management.Steps to increase network efficiency with GIS and modeling include several tasks.First, all of network data including spatial and attributed information was stored in a GIS system.Then using a specific extension tool (such as Hydrogen), this data was imported into the EPANET program.After hydraulic calculations to identify high-pressure zones in the network, a layer in the GIS was created to represent these zones.To verify the general results of the model, we used information that was collected during real incidents in the network.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".