Comparing Rainfall Dependent Inflow and Infiltration Simulation Methods
Bibliographic record
Abstract
Rainfall dependent inflow and infiltration (RDII) is a significant, though undesirable, component of the m•ban wet-weather water budget in many sanitary sewer systems.Costs and environmental damage attributable to RDII are significant.Costs may be accrued tlu•ough increased treatment and conveyance cost-:, increased maintenance costs, and sanitary sewer overflows (SSOs ).To reduce these coste; and mitigate environmental damage, engineered solutions require estimations of the long-term characteristics of the RDII response to wet weather.This in turn requires estimation of the performance of the existing collection and treatment system, as well as the expected perfonnance of various possible solutions.Due to the complex number of available pathways that RDII may enter a sanitary sewer, RDII is one of the most difficult components ofthe urban wetweather water budget to estimate.RDII observations typically indicate response times that may range from several minutes to several days or weeks.This is confounded by regional and/or seasonal groundwater trends that influence RDII response.Various tools have been applied to estimating this special hydrologic response, including the rational method and several unithydrograph methods.This chapter provides an overview of available RDII estimation methods and highlights results from arelativelynewphysically based conceptual method first introduced by Kadota and Djebbar ( 1998).Kadota and Djebbar (1998) reported on modifications to the USEPA SWMM RUNOFF model that include the response of a conceptual non-linear reservoir to changes in groundwater elevations resulting from permeable-area infiltration.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".