Optimal sizing of rainwater harvesting systems for domestic water usages: A systematic literature review
Bibliographic record
Abstract
Rainwater harvesting systems (RWHS) are increasing in popularity because of their ability to alleviate water pressure on centralized systems, minimize or delay rainfall runoff, and fit relatively easily in both the centralized/decentralized infrastructure organization. Adequately sizing RWHS is critical to optimizing their operation because under-sizing results in systems that are unable to provide a sufficient, reliable source of water while oversizing increases the capital costs incurred with limited marginal benefits and poses potential water quality risks. In this paper, we conduct a systematic literature review to assess the state-of-art in the field of optimization of domestic rainwater harvesting systems. Sizing of storage is identified as the most important objective of optimization, yet sizing for cost is the most frequently implemented outcome of optimization. Optimizing for a local maximum is often favored over simulation-based optimization methods that produce global maxima. To derive more realistic sizing estimates, future optimization studies will have to take into account greater variation in water demands as well as various climate change scenarios, especially given that rainfall frequency and quantity are critical design variables of a rainwater harvesting system.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".