Hybrid Wavelet and Local Approximation Method for Urban Water Demand Forecasting – Chaotic Approach:
Bibliographic record
Abstract
Water demand forecasting is a nonlinear and non-stationary process. Different variables affect the value of demand directly or indirectly. These variables have non-stationary behavior relates to the demand time series, which should be considered in forecasting of water demand. Thus, in the first stage, chaotic behavior of water demand data was investigated to determine the number of effective variables by the value of correlation exponent. Then, the nonlinear process of water demand was forecasted by nonlinear local approximation (NLA) method using reconstructed phase space of input variables. Next, to improve the accuracy of the method, selected embedding dimension of input variables was decomposed by wavelet transformers. The application of phase space reconstruction (PSR) and wavelet decomposition were compared with the results of the models without pre-processing to evaluate their application in improving the models’ accuracy in forecasting water consumption of Kelowna City (BC, Canada). The results showed that NLA forecasted more accurate than the results in previous literature in the same case study.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".