Selection and Evaluation of Integrated Solutions in a Combined Sewer Network
Bibliographic record
Abstract
Combined sewer overflow (CSO) control strategies are commonly selected relying on optimization or multicriteria analysis. A methodology is proposed in this paper for selecting and evaluating the solutions to mitigate stormwater problems in combined sewer systems (CSSs) based on the understanding and rigorous diagnosis of the causes of the observed problems using hydrological/hydraulic simulations. The methodology was applied to a sewer catchment near Montreal (Canada) in order to reduce the magnitude and recurrence of sewer surcharge and local flooding without increasing the recurrence of CSOs. Two types of control strategies were considered: (1) large-scale solutions, i.e., redirection of stormwater inflows and separation of the CSS; and (2) source control solutions including both green and gray infrastructure. These solutions were evaluated through a comparative analysis of nine scenarios. Based on the results, source control solutions could be considered as the most effective interventions on the studied catchment for eliminating or significantly reducing the occurrence of sewer surcharge (from an average of 6.7 to 2.6) and surface flooding on local streets (from an average of 3.5 to 0.4 per year). The developed methodology is a valuable tool to be applied to other urban drainage catchments that encounter problems similar to those observed on the studied catchment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".