Modeling Intermittent Water Supply in SWMM: A Critical Review With Reproducible Recommendations and a Python Package
Bibliographic record
Abstract
Abstract Distinctly, Intermittent Water Supply networks cycle between filling, pressurized supply and draining, leading users to withdraw water and store it for later consumption. While intermittent networks serve one in five piped water users, their characteristic hydraulic features cannot be readily represented in available, open‐source hydraulic modeling software. Several hydraulic modeling methods have been proposed in the literature, but these methods disagree in their construction and assumptions, and most are not reproducible, hindering the exploration of techniques to improve the quality and equality of service in intermittent networks. To improve the reproducibility, consistency, and numerical stability of hydraulic models of intermittent supply, we synthesize the best modeling practices in the literature into a recommended, reproducible method: SWMM for Intermittent Networks (SWMMIN). We outline and demonstrate how SWMMIN models network pipes and user behavior: withdrawing water subject to available pressure in the network, storing water, and consuming from storage for their various activities. For experienced IWS modelers, we provide quantitative evidence of numerical stability and mass conservation within SWMMIN (from >1,000 simulations of 3 network models); we recommend spatial and temporal discretizations that result in solution speeds between 40 and 200 m/s. To facilitate the adoption of our recommended modeling procedures, we share a Python package (GOSWMMIN) that automates the implementation of SWMMIN. Lastly, we propose a model reporting template to bolster reproducibility and call on fellow modelers to use it; accessibly and reproducibly described models of intermittent supply have the potential to accelerate research and transform practice.
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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 | 0.041 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.012 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".