SWANP: A Comprehensive Tool for Optimized Water Network Clustering
Bibliographic record
Abstract
The creation of district metered areas (DMAs) in water distribution networks (WDNs) is a widely used technique that enables utilities to monitor systems by dividing them into subsystems. This approach effectively supports water balance, pressure management, and protection in case of contamination. The optimal design of DMA can be achieved with the use of advanced algorithms and tools, defining the partitioning of WDNs, which is structured in clustering and dividing phases. This research presents an application of SWANP (Smart Water Network Partitioning and Protection), a cutting-edge software collaboratively developed by researchers, an academic spin-off and water utilities. SWANP enables advanced hydraulic analysis using pressure-driven and demand-driven analyses to optimize WDNs. It offers various partitioning options through a user-friendly GIS-based interface, providing utilities with accessible, automated solutions for network management. In this paper, SWANP software is used to define a preliminary topological partitioning with reference to 38 WDNs, showing improvements in partitioning strategies also without hydraulic simulations. Results indicate cost savings on flow meters and valve installations, along with better control of water distribution systems. SWANP represents a major advancement in water network management, offering utilities innovative solutions for optimizing performance and ensuring sustainable, resilient urban infrastructure.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".