Modeling And Analysis Of Rainwater Harvesting Systems Under Different Climates
Bibliographic record
Abstract
There is a strong interest in rainwater harvesting (RWH) solutions as the global demand for water increases and water sources face contamination and depletion. Despite the extensive research conducted on the impact of RWH on watersheds, there is significant research to be completed to determine the relationship between the collection tank volume, roof size, and water demand satisfied by the RWH system. This thesis aims to further the understanding of the behaviour of RWH systems through a quantitative assessment of the water provided by these systems. Calculating the reliability of RWH systems in various Canadian regions allows for an evaluation of the capacity of RWH in meeting various residential water needs in Canada’s diverse climates. The results are obtained through hourly continuous simulation to provide the most accurate results and are presented in a user-friendly format through simple equations and graphs. RWH modeling through analytical equations do not require long-term historical data and are easier to use than conducting computer-aided continuous simulations. A better understating of the analytical equations’ application is developed through a comparison between the analytical and continuous simulations methods. The comparison is held for different regions within Canada, and the analysis confirms a lack of accuracy for the analytical method in some climatic conditions. Daily continuous simulations conduced for Ugandan and Canadian regions provide a perspective on the feasibility of RWH systems to meet the human right to drinking water in the two countries. A comparison of the reliability of RWH tanks in Ugandan and Canadian regions is conducted to provide insight into the impact of rainfall patterns on the reliability of RWH systems. The evaluation of RWH performance in the RRM context in Canada and Uganda is aimed to address the lack of adequate water sources in rural, remote, and otherwise marginalized (RRM) communities globally. Examining the most accurate and appropriate modeling tools and assessing the actual yield of RWH systems provides information critical to water-sensitive communities and provides a foundation for future research to further explore the most effective application of RWH in urban and water-sensitive communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 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 teacher head, 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".