Generation and Validation of Synthetic WDS Case Studies Using Graph Theory and Reliability Indexes
Bibliographic record
Abstract
Finding case studies that give statistical significance to the conclusions of research on Water Distribution Systems (WDS) can be a challenging task. The generation of synthetic (virtual) WDSs has been proposed recently to tackle this difficulty. These methods try to generate realistic data, based on different assumptions for different properties of the networks. This paper describes the use of a method for the generation of synthetic distribution systems and its subsequent comparison against real life systems to validate the suitability of the synthetic set to drive the conclusions of future research. Focus was given to connectivity and reliability-related indexes considering the future use of these synthetic WDSs to study relationships between connectivity, reliability and energy consumption. The algorithm for the generation of synthetic WDSs was based on the work, methods and software presented by Mair et al. [1] . The validation procedure was made by evaluating metrics or indexes that account for network connectivity and system reliability and comparing their ranges in both sets. Early results showed that the synthetic WDSs required an enhancement of network connectivity to make them more similar to real-life systems. After implementing a routine to increase the meshedness of the networks, an acceptable degree of similarity between the synthetic and the real-life sets of WDSs was achieved, although some modifications to the networks may be required in the future.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".