Contaminant intrusion in water distribution systems : advanced modelling approaches
Bibliographic record
Abstract
Since exposure to contaminants may have direct adverse impacts on public health, contaminant intrusion has been recognized as one of the top priority in drinking water supply research. Three components must exist to cause contaminant intrusion into a water distribution system. These include the availability of source(s) of contaminant(s) around a water distribution system, the existence of driving forces (low/negative pressure) to make a contaminant enter into a water distribution system, and the presence of pathway(s) through which contaminant(s) intrude into a water distribution system (WDS). Exposure assessment is the most challenging part as location of contaminant intrusion, rate of intrusion, and the fate of contaminants within WDS need to be estimated accurately. In this dissertation, first, common uncertainty analysis techniques are discussed in the context of conservativeness, execution time, ease of formulation, and complexity. Second, a fuzzyrule based model has been developed to identify contaminant intrusion potential in a WDS. The potential of contaminant intrusion has been determined by integrating the potentials for contaminant sources existence, driving forces, and pathways. Third, a novel ingress model has been developed for more realistic estimation of intrusion rate by taking into account the effects of surrounding soil on intrusion rate. Coupled with an Eulerian-based transient hydraulic model, a Lagrangian transient water quality model is developed to predict the fate of the contaminant throughout a WDS. The proposed models are applied to case studies available in the literature to investigate the applicability of the models. The proposed models enhance the reliability and safety of WDSs by improving the prediction ability of the existing modelling tools.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".