Analytical Urban Storm Water Quality Models Based on Pollutant Buildup and Washoff Processes
Bibliographic record
Abstract
This paper presents methodology and major procedures for the development of analytical urban storm water quality models following derived probability distribution theory, which involves conceptualization of the three major components, i.e., the rainfall–runoff transformation, pollutant buildup, and washoff processes. In this study, two different types of the rainfall–runoff transformations are employed in an attempt to improve model performance by considering spatial variations of parameters associated with runoff generation mechanisms. By integrating different types of the rainfall–runoff transformations and pollutant buildup function with washoff function, two different types of pollutant washoff load models are formulated. Thereby, the probability distributions of the rainfall characteristics are mathematically transformed to create system storm water quality control measures, such as the average pollutant event mean concentration and long-term pollutant loads to receiving waters. These storm water quality control measures are closed-form analytical models and can be employed as alternatives to continuous simulation models for the evaluation of long-term system behavior. The results from case study reveal that with appropriately formulated rainfall–runoff transformation along with pollutant buildup and washoff functions, analytical storm water quality models are capable of providing comparable results to observed data and can serve as effective tools for storm water quality control analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".