Urban water demand assessment for sustainable water resources management, under climate change and socioeconomic changes
Bibliographic record
Abstract
Abstract The relationship between water abstraction and water availability has turned into a major stress factor in the urban exploitation of water resources. The situation is expected to be sharpened in the future due to the intensity of extreme meteorological phenomena, and socio-economic changes affecting water demand. In the city of Volos, Greece, the number of water counters has been tripled during the last four decades. This study attempts to simulate the city's network, supply system and water demand through a forecasting model. The forecast was examined under several situations, based on climate change and socio-economic observations of the city, using meteorological, water pricing, users' income, level of education, family members, floor and residence size variables. The most interesting outputs are: (a) the impact of each variable in the water consumption and (b) water balance under four management scenarios, indicating the future water management conditions of the broader area, including demand and supply management. The results proved that rational water management can lead to remarkable water conservation. The simulation of real scenarios and future situations in the city's water demand and balance, is the innovative element of the study, making it capable of supporting the local water utility.
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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 | 0.001 |
| 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".