Extending Municipal Water Demand Forecasting Capacities by Incorporating Behavioural Responses
Bibliographic record
Abstract
Growing urban water demands are putting increasing pressure on the infrastructure of many water agencies, signaling the potential need for greater capital investments. Most water agencies forecast demand by multiplying future population estimates with historical per capita water use. However, this approach tends to be inaccurate by failing to account for other demand drivers, such as income, price and household appliance holdings. Providing water agencies with enhanced water forecasting capabilities that better reflect water users’ behaviour is one way these agencies can address this challenge. The proposed study builds upon the following initial project: Project (2008-2012): More Value from the Same Water: Maximizing Water's Sustainable Contribution to the Canadian Economy, Diane Dupont, Brock University This project sought to advance understanding of the factors (i) governing water use, (ii) influencing water recirculation decisions, and (iii) influencing adoption of residential water conserving technologies. The project produced statistical models of households' and manufacturing firms' water use. As an extension of this research, the proposed project is based on observations made by a number of experts in the municipal water sector. Specifically, it has been observed that water conservation departments are not always involved in demand forecasting, since this is done on the basis of infrastructure requirements. Previous experience in the energy industry has demonstrated how costly it can be to overbuild supply networks based on faulty demand forecasts that have failed to incorporate behavioural responses related to conservation. Thus, there is a need to bring together the behavioural sciences that underlie efforts to promote water conservation in the short-term with longer-term water demand forecasts.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".