Improved Water Demand Forecasting to Promote Sustainable Water Management
Bibliographic record
Abstract
The Region of Durham in Ontario is a fast growing urban area east of Toronto and has a population of 650,000, covering an area of 2500 km2. It has a single tiered water supply system with the regional agency acting as a retailer to provide water to households, businesses, institutions and farms. In 2014 its output was 63,555 Mega Litres. The Region of Durham’s water agency faces many challenges including growing demands, ageing infrastructure, water quality concerns and rising costs operations. Forecasting water demands on a daily basis is remarkably difficult. Variables such as weather conditions, operational changes, watermain breaks, business cycles, human behaviour, economic and social factors effect water demand forecasting, but it is difficult to quantify those factors and thus difficult to make an accurate prediction. The water industry has responded to this challenge by developing sophisticated procedures for forecasting. The approaches used include Artificial Neural Networks (ANN) and time series statistical modeling, which takes into consideration all possible factors as input variables to build forecasting model. The Region of Durham has thus far relied upon ANN with mixed results. Through several years of observation, overall the ANN forecasting model can predict a relatively accurate water demand for next 24 hour period (R2 >0.7) in some pressure zones. Winter forecasting is more accurate than summer because outdoor water use is extremely variable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".