Water distribution network sectorisation using graph theory and many-objective optimisation
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Bibliographic record
Abstract
The water distribution network (WDN) sectorisation problem is characterised by structural and hydraulic requirements that make existing graph partitioning techniques inadequate to find a good solution. Specifically, sector isolation and direct access to at least one source for each sector are not addressed. This study proposes a method to address structural requirements of water network sectorisation with minimum negative impact on the hydraulic requirements. This paper first elaborates the sectorisation problem and discusses the requirements of water network sectorisation. Then, it proposes a novel method, called WDN-Partition, which applies a new heuristic structural graph partitioning algorithm, combined with a many-objective optimisation procedure, to find near-optimal arrangements of nodes into sectors. The criteria of optimisation and their priorities can be specified for each case. The outcome of the method is a set of non-dominated sectorisation solutions, ranked lexicographically based on their values for the chosen criteria and their priorities, from which the final decision can be made by the domain experts. WDN-Partition has been implemented and integrated with a hydraulic network simulator. The simulation-based evaluation results demonstrate that WDN-Partition generally achieves its design objectives to partition a water network into isolated sectors with a minimal negative impact on the hydraulic performance criteria of the network.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it