Modeling Stormwater Runoff from an Urban Park, Singapore Using PCSWWM
Bibliographic record
Abstract
The environmental and societal benefits of urban green space have long been recognized, but such land use often becomes a secondary consideration in urban drainage modeling, in part because it is more difficult to obtain site specific data for model calibration. Singapore has re-branded itself from garden city to city in a garden and as such parks, urban forests, and even agricultural areas are particularly important in sustaining the liveability of this highly urbanized city-state. The objective of this study was to quantify the rainfall-runoff processes for a park in Singapore and explore the efficacy of PCSWMM in estimating runoff from a nearly 100% pervious area. Admiralty Park consists of two sections, one being a traditional urban grassed area with trees, outdoor exercise areas, and promenades; and the other being a forested nature trail that includes a mangrove habitat opening to the Straits of Johor. Infiltration rates, using a double O-ring infiltrometer, were measured at five sites within the traditional grassed area and samples for textural analysis were collected at the same sites. This part of the park is serviced by a tile drain and an ISCO 2150 area-velocity meter was installed near the drain outlet, together with a tipping bucket rain gauge, to monitor rainfall and runoff between 2013-12-21 and 2014-05-04.
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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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 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".