Modelling consumers in intermittent water supplies: a comparative review of EPANET - based methods
Bibliographic record
Abstract
Intermittent Water Supply (IWS) networks, which pressurize for less than 24 hours/day, affect 1 billion people worldwide and are associated with increased water contamination and inequitable distribution. Due to these downsides, various methods to model consumers and understand their behaviour were proposed. We found more than 8 different methods of modelling IWS consumers, but we know of no comparative analyses of these methods nor their efficacy. This study comparatively reviews methods of modelling IWS consumers implemented in EPANET, due to their prevalence, reproducibility, and accessibility. Methods of modelling IWS consumers were found to fall into three groups based on their assumed consumer withdrawal behaviour: unrestricted, flow-restricted, and volume-restricted methods. We applied each method to three reference networks and compared the methods’ performance after subjecting each reference network to common IWS improvement strategies, including changing the supply duration and/or source pressure. Flow-restricted methods assume consumers withdraw their demands at a constant rate, leading to unrealistic predictions when subjected to unexpected changes in supply conditions. Volume-restricted methods assume consumers withdraw at the highest, hydraulically feasible rate until their storage tanks fill. This assumption highlights pronounced inequality between consumers, as consumers advantaged by source proximity and/or elevation receive their demands faster and earlier. Our results demonstrate that the simulated behaviour of IWS depends substantially on the type of consumer model employed. Presented examples demonstrate that consumer model selection can change the simulation-predicted optimal strategies for coping with and improving IWS. IWS modelling methods should reflect the consumer behaviour in the modelled network and the model’s intended use.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".