Optimal design of district metered areas based on improved particle swarm optimization method for water distribution systems
Bibliographic record
Abstract
Abstract Although partitioning of water distribution systems (WDSs) into district metered areas (DMAs) is challenging, it can be effectively used for refined management and leakage control. A two-step novel process for DMA partitioning is proposed in this study, i.e. clustering and dividing. The first step is to cluster nodes through an improved METIS graph partitioning method. The second step is to optimize the location of flowmeters and gate valves on boundary pipes by obtaining the feasible solutions. The good solutions that constitute the Pareto front were produced, which could be a tough and time-consuming task. The paper proposes the innovative and efficient dividing phase: (a) selecting the important boundary pipes by hydraulic analysis; (b) using the improved particle swarm optimization algorithm; (c) proposing three objective functions. The proposed method is applied to Modena and EXNET networks to demonstrate its feasibility.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".