Challenges and Opportunities in Modelling a Regional Water System with Interchangeable Sources
Bibliographic record
Abstract
Metro Vancouver (MV) serves over two million people and comprises 22 member municipalities, one electoral area and one treaty First Nation. MV provides a variety of services to its members, including regional water supply treatment and transmission. The primary sources of water supply are the Capilano, Seymour and Coquitlam reservoirs. The Seymour and Capilano reservoirs supply up to 70% of MV's drinking water, with the Coquitlam Reservoir supplying the remainder. MV's regional supply system consists of 550 kilometres of water mains, 14 pump stations, and 20 peaking reservoirs. Three primary chlorination treatment stations, six pressure regulating sites and 113 metering stations are also included in the system. Historically, any one of the three sources could be removed from service under winter demands. This has sometimes been necessary to address periodic episodes of elevated turbidity resulting from winter storms. However, the system dynamics will change significantly with the commissioning of a new filtration plant located in the Seymour area. Water from both the Capilano and Seymour sources will be mixed and filtered at the Seymour Capilano Filtration Plant (SCFP) and transmitted to the system. The new SCFP will ensure that clean drinking water can continue to be provided via both the Capilano or Seymour headworks during a turbidity event in either of those reservoirs. This step change in system operation, however, will alter the hydraulic heads delivered by the Capilano and Seymour systems, hence posing a significant change in system dynamics. Due to the highly dynamic and complex nature of MV's regional water supply system, a hydraulic simulation model of the water system was developed using EPANET software to assist in system assessment. This paper outlines the challenges in using this model to develop both short-term operational strategies and long-term planning strategies. After presenting an overview of model development, calibration, verification and scenario simulation, two case studies will be discussed in this paper: (1) Investigating options for supplying a member municipality, the City of Richmond, with temporary alternative sources due to supply disruptions during construction of a major subway line which severed their primary feed; and (2) Reviewing options to defer a second supply intake at Coquitlam by maximizing supply from the Capilano and Seymour sources.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".