A Statistical Frequency Analysis of Rainfall Derived Inflow and Infiltration in Sanitary Sewer Systems
Bibliographic record
Abstract
Rainfall Derived Inflow and Infiltration (RDII) is a substantial contributor to sanitary sewer flows, yet Ontario’s commonly applied uniform design allowance of 0.26 L/s/ha does not account for variability across systems (particularly in characterizing flows in small sewersheds). This research evaluates the adequacy of this standard using York Region flow monitoring data and develops a return-period-based, data-driven alternative. In this research, RDII events are isolated using a refined Event-Based Approach and screened to ensure the dataset is defensible for modelling. Four probability distributions are tested, with the Log-Pearson Type III emerging as the most suitable for extrapolating RDII magnitudes to 100-year return periods. The results show that most 25-year RDII values exceed the current allowance, particularly in small sewersheds. A power-law equation relating RDII to sewershed area is proposed, producing more representative flows for both small and large systems. This ensures that smaller areas, which are often underestimated by the uniform approach, receive proportionally higher RDII estimates. The framework enables local calibration and improved design verification and planning practices, offering a scalable and risk-aware approach to RDII management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".