Urban Flood Modeling of a Partially Separated and Combined Drainage System in the Grefsen Basin in Oslo, Norway
Bibliographic record
Abstract
The Storm Water Management Model (SWMM) has been globally used for stormwater management. However, the calibration and evaluation of SWMM for historical rainfall–runoff events in partially separated and combined drainage systems is rarely reported in Norway. In this study, we employed SWMM for the Grefsen catchment in Oslo, Norway. The main problem in the Grefsen basin is combined sewer overflow. We calibrated the model parameters based on 32 rainfall–runoff events and evaluated the calibrations using four indicators: Nash–Sutcliffe efficiency, percentage bias, and continuity errors for runoff and flow. There were 32 successful calibrations using Nash–Sutcliffe efficiency, 30 successful calibrations using percentage bias, 32 successful calibrations using continuity error runoff, and four successful calibrations using continuity error flow. SWMM can well simulate the dynamics of hydrological and hydraulic systems in this catchment. Among the 124 validations, there were 88 successful simulations using Nash–Sutcliffe efficiency, 35 successful simulations using percentage bias, 124 successful simulations using continuity error for runoff, and 62 successful simulations using continuity error for flow. The results show that percentage bias and continuity error flow are the critical indicators for model calibration. This study reveals the large uncertainty caused by calibration and validation criteria, and highlights the importance of considering model computation error.
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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 | 0.003 | 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 | 0.001 |
| 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".