Stochasticity of Demand and Probabilistic Performance of Water Networks
Bibliographic record
Abstract
Accurate prediction and modeling of water demand is crucial to understanding the long-term performance of systems, as it is to mounting an effective design and operational planning effort. The paper investigates the influence of cross correlation and autocorrelation in demand on the probabilistic, hydraulic performance of water networks, as measured with the mean and variance of nodal pressures. A stochastic demand model that accounts for lag-1 autocorrelation and lag-0 cross correlation between demands is applied to generate synthetic series of correlated demands. A Monte Carlo Simulation is coupled with EPANET2 to generate time series of pressures and update the mean and variance of nodal pressures. Preliminary results indicate that enforcing a strong lag-0 cross correlation in demand decreases the mean of pressures and increases the variance of pressures. This indicates that the frequency of low-pressure, hydraulic failures is contingent on the level of correlation measured or assumed in a reliability study. Enforcing a strong lag-1 autocorrelation memory at system nodes produces little or no changes in the mean and variance of nodal pressures, but it is found to govern the period of time a pressure signal can persist below a minimum-pressure constraints and remain in a hydraulic failure state.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".