Short‐term municipal water demand forecasting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Water demand forecasts are needed for the design, operation and management of urban water supply systems. In this study, the relative performance of regression, time series analysis and artificial neural network (ANN) models are investigated for short‐term peak water demand forecasting. The significance of climatic variables (rainfall and maximum air temperature, in addition to past water demand) on water demand management is also investigated. Numerical analysis was performed on data from the city of Ottawa, Ontario, Canada. The existing water supply infrastructure will not be able to meet the demand for projected population growth; thus, a study is needed to determine the effect of peak water demand management on the sizing and staging of facilities for developing an expansion strategy. Three different ANNs and regression models and seven time‐series models have been developed and compared. The ANN models consistently outperformed the regression and time‐series models developed in this study. It has been found that water demand on a weekly basis is more significantly correlated with the rainfall amount than the occurrence of rainfall. Copyright © 2005 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it