A GIS based spatial decision support system for analysing residential water demand: A case study in Australia
Bibliographic record
Abstract
Highlights - This paper develops GIS based SDSS for predicting residential water demand. - The paper discusses the determinants of water demand. - The developed model is flexible for spatial and temporal changes. - The paper provides capacity to analyse alternative policies. Abstract Managing water resources and the need to adapt both supply and demand side policies to a changing environment has become a priority in both developed and developing countries. This research demonstrates the application of the geographic information system (GIS) in modelling residential water demand in order to develop a spatial decision support system (SDSS). Household level survey data covering 90suburbs within the Brisbane City Council (BCC), Queensland, Australia, are used for the analysis. First, residential water demand was estimated and the most significant variables found to predict high water use at the suburban level. These variables included household size, presence of a swimming pool, income and people over 65 years of age. By integrating this model with an SDSS, a spatial decision support system for residential water demand ( SDSS-RWD ) is developed. By producing maps which clearly display the different factors affecting residential water demand, the benefit of the SDSS-RWD is found in its use as a policy making tool for manipulating and evaluating effective water management strategies. In particular, the flexibility of the SDSS-RWD offers in evaluating changing determinants of residential water demand creates the capacity for local government bodies to analyse a range of alternative policies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".