Characterizing Rainfall Derived Inflow and Exploring Lot-Level Based Stormwater Management Modelling Techniques with Low Impact Development
Bibliographic record
Abstract
Rainfall Derived Inflow (RDI) as a part of Rainfall Derived Inflow and Infiltration (RDII) has been known to cause various issues around the globe. Common issues resulting from excessive RDI range in magnitudes, from residential basement flooding to urban city flooding. Significant effort and time have been spent to attenuate RDI and reduce the risk of flooding which causes extensive environmental and financial losses. While continuing the effort in reducing RDI/RDII, it is difficult to characterize its volume accurately. In this research, using a local community as a case study site, a novel method is presented to characterize RDI with high accuracy. A general guidance is developed for engineers to determine the data size needed to correctly estimate RDI in the future. Furthermore, a Storm Water Management Model is created to examine the efficiency of Low Impact Development LID devices at lot-level, including its performance under climate change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".