Spatial Analysis Tool for Development of Leakage Control Zones from the Analogy of Distributed Computing
Bibliographic record
Abstract
Development of a methodology, rational and zoning scheme for the demarcation of complicated water distribution networks is important for making the zone demarcation process more accurate, economical, less time consuming, fast, repeatable, generic and optimal with respect to the cost of flow meters required. A new water distribution zone demarcation method is presented that uses the analogy of graph theoretic and graph partitioning principles used in distributed computing to distribute workloads among processors to suggest optimal zoning schemes based on balancing length, demand or flow within zones with the objective of monitoring of unaccounted for water. The method is developed into an automated prototype optimal zoning tool using C++ programming language, a graph partition tool used in distributed computing (METIS) and the EPANET tool kit. The tool is operated by a user interface written in the Python. Case studies are presented to demonstrate how the zoning tool is applied to the zone demarcation problem for the developed zoning schemes. The developed zone demarcation tool was observed to be an efficient and effective approach for the optimal demarcation of complicated water networks into optimal zones based on balancing length, demand or flow within zones. However the tool is sensitive to the number of partitions, the topology of the Water distribution network and the partitioning algorithms used. The tool can be used as a decision support tool for the optimal development and reduction of uncertainties in development of leakage control zones by decision makers. This will enable water companies to increase their productivities and also optimise resource allocations by reduction of the time to monitor, discover leaks and partition zones. This will lead to improved operating revenues.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".