Calibration of Distributed Rainfall-Runoff Model in Hamilton County, Ohio
Bibliographic record
Abstract
Appropriate parameter estimation for the assessment of green infrastructure alternatives in combined sewer systems (CSS) is becoming more important in rainfall-runoff modeling.This chapter studies the calibration of eleven individual storm events in a distributed rainfall-runoff model in SWMM5 for the assessment of green infrastructure in a combined sewer system.High detail rainfall and flow measurements were used.Parameters that represent the area in the model were assumed fixed in this study.The model independent parameter estimation (PEST) method was used for both parameter estimation and sensitivity analysis.A base flow that includes dry weather flow (DWF) and ground water infiltration (GWI) improved the performance of the simulated hydrographs in the CSS.This study shows that there was not unique set of parameters values that can be used for simulation of the single storm events selected.There was significant variation of the calibration parameter values.Roughness and depth storage in the impervious surfaces were the more sensitive parameters within the calibration process.The sensitivity analysis identified high non-linearity due to high correlation between parameters.There is a systematic bias in the model that needs further research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".