Application of the TONIC model to assess the effectiveness of green roofs for a combined sewer network in Thu Duc City
Bibliographic record
Abstract
Urban flooding in cities in Vietnam presents a complex challenge from natural occurrences and human activities. This research investigates the effectiveness of nature-based solutions (NbS) in fostering sustainable urban water management. The study utilized the TONIC model (Tools fOr greeN resilient Cities) to evaluate spill events within the combined sewer system of a drainage catchment in Thu Duc City, Ho Chi Minh City, Vietnam. The occurrence and magnitude of spills will likely escalate with the expansion of non-absorbent surfaces, indicating a pattern of unsustainable urban growth. The implementation of Green Roofs (GR) as an NbS measure was examined. In 2020, there were 16 days with stormwater spills totaling 3,035 m³. However, introducing GRs significantly decreased total discharge, from 2.78 million m³ to 2.17 million m³, and eliminated spill days in the study area. GRs effectively reduced impermeable areas and runoff coefficients (RC), achieving a 22% reduction in discharge volume and preventing spills during heavy rainfall. Although the real-world accuracy of these findings may vary, the study underscores the potential of NbS to improve urban water management practices. Thus, the TONIC simulation highlighted the benefits of GRs in lessening the hydraulic pressure on the sewer system and reducing pollution in urban canals due to overflow.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".