Forecasting Dubai City water demand using the fuzzy logic approach
Bibliographic record
Abstract
The United Arab Emirates is one of the largest global consumers of desalinated water. Therefore, it is imperative to properly manage current water sources while planning for future demand and operation. One important management tool is to forecast future demand from past and present water demand. One potential prediction tool is the fuzzy logic method that can be used to model nonlinear data. One of the main advantages of the fuzzy logic method is that it does not carry many assumptions, ergodicity and stationarity of other statistical techniques. This research utilises the Mamdani approach to predict the water demand from three antecedent water consumption values, with the model analysed using the MATLAB software for four different membership functions, namely Triangular, Trapezoidal, Gaussian and the Generalised bell-shaped membership function. The analysis highlighted that the triangular, trapezoidal and generalised bell-shaped membership functions indicated a minimum error, with the Gaussian membership function demonstrating results removed from the other three membership functions. The research concludes that utilising hybrid models, improving the quality of data and utilising a robust set of rules can improve the model's performance in predicting water consumption.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".