Agent-Based Modelling as a Decision Support Tool for Water Resources Planning and Management
Bibliographic record
Abstract
The research presented in this thesis focuses on developing a water-use forecasting method using an agent-based model. The novelty of this method is that it allows for a given population to be represented heterogeneously and that the household are connected amongst themselves and can transmit information and modify their behaviours. The model is showcased using a case study in Kingston, Ontario where households are modeled at the individual level as agents that separate water use into 6 household fixtures. Agents are given a set of attributes that enable them to make decisions and adapt behaviour based on social-networks and communication. Available data from Statistics Canada is used to characterize 40 neighbourhoods within Kingston, Ontario. The modelling framework is utilized to test population responses and potential water savings achieved through conservation campaigns. The research consists of evaluating the change in water demand using the model and running the results into a pipe-network hydraulic solver (EPANET 2.0) to calculate the change in energy use from the distribution system associated with conservation programs. The model and the case study are used to answer a series of research questions concerning the sensitivity of the ABM to social communication parameters, the potential water and energy savings that are achievable in the Kingston distribution system and finally, how the spatial distribution of water savings affects energy savings in the Kingston system.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".