Bioretention Model for Urban Runoff Treatment in a Tropical Climate: A Case Study at the Universiti Sains Malaysia
Bibliographic record
Abstract
Model for Urban Storm Water Improvement Conceptualism (MUSIC) software has been widely used to predict the treatment and performance of stormwater Best Management Practices (BMPs) such as bioretention for decision-making purposes in stormwater management. However, the calibration of bioretention models based on pollutant runoff characteristics in the tropics is rarely studied. This paper presents the calibration of bioretention model parameters using MUSIC software to treat polluted runoff in a tropical climate. The bioretention model was simulated based on a pilot study at the Universiti Sains Malaysia (USM) engineering campus to evaluate the flow rate and pollutant’s reduction performance. Two stages of calibration were conducted, with the first stage to calibrate the inflow and pollutant concentrations, and the second stage to further calibrate the k-C* model to fit the experimental results. The validation of the model was done using the percentage bias between modeled and experimental data to evaluate the accuracy of bioretention modeling using MUSIC software. Overall, the accuracy of this model increased after calibration and can be accepted, as the performance of bioretention models for total suspended solids, total nitrogen, and total phosphorus removal percentage are good or very good (-13%, -4%, and -39% respectively), whereas the flow rate reduction is satisfactory (17%).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".