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Record W2997456163 · doi:10.2166/ws.2019.199

Urban water demand assessment for sustainable water resources management, under climate change and socioeconomic changes

2019· article· en· W2997456163 on OpenAlexaff
Angelos Alamanos, Stamatis Sfyris, Chrysostomos Fafoutis, Nikitas Mylopoulos

Bibliographic record

VenueWater Science & Technology Water Supply · 2019
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWater conservationDemand managementWater resourcesWater supplyWater balanceEnvironmental scienceClimate changeResidenceWater pricingWater resource managementWater useSocioeconomic statusBusinessEnvironmental economicsNatural resource economicsEnvironmental resource managementEnvironmental engineeringEconomicsEcologyEngineering

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.204
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2019
Admission routes1
Has abstractyes

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