Smart Rainwater Storage - Development of a Smart Meter Prototype Enabling Local Rule-Based Predictive Real-Time Control and User Interaction
Bibliographic record
Abstract
Environmental and anthropogenic factors pose a significant challenge to stormwater management and infrastructure in urban areas. Rain storage tanks, a low-impact development technique, aim to deal with precipitation before it enters the drainage system as stormwater runoff and thus reduce the impact on the infrastructure and combined sewer overflows. The MRP develops a prototype of a smart meter measuring mainly the water level in a rainwater storage tank with low-cost off-shelve components steered by two variants to control a smart storage tank in automated real-time control or steered by the user. The concept is conducted in an experimental set-up and verified. The development aims to enhance decentralized stormwater infrastructure toward reducing total runoff volume and peak flow. In the market-based system, the motivation for the user is the dynamically priced discharge fee aiming to stimulate retention by the landowner and provide a financial incentive. The research findings guide modellers in developing a smart meter using a local rule-based real-time control to manage a rainwater storage tank, including the communication and steering through the user. In future research, machine learning should be used to optimize the prototype.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".