Realtime Water Depth Logger Data as Input to PCSWMM to Estimate Tree Filter Performance
Bibliographic record
Abstract
In this study, water depth measurements were collected in storm water infrastructure during rain events using a pressure based water level sensor. Irregularities within the measured water level datasets required data smoothing to prepare the observed data for calibration. A rainfall-runoff model was created using a proprietary version of the U.S. Environmental Protection Agency's Storm Water Management Model, PCSWMM, to predict the performance of recently implemented green stormwater infrastructure with respect to runoff at the site. The PCSWMM model calibration was accomplished by comparing water level data collected on site to the PCSWMM output data produced by the uncalibrated model. Nash-Sutcliffe efficiency was used to assess the performance of the calibration procedure. Sensitivity analyses of the estimated parameters were performed to assess the impacts of the model parameters on overall model output. The overarching objective of the study was to demonstrate the value of inexpensive and readily available real-time pressure based water level sensor data to calibrate a PCSWMM model.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".