A Detailed Procedure for Separating RDII Stages and Generating a Single Set of RTK Hydrographs for Continuous Simulation
Bibliographic record
Abstract
This chapter presents a step-by-step procedure for performing continuous calibration of the rain dependent inflow and infiltration (RDII) process using the RTK method in SWMM.The procedure starts with the separation of the observed RDII hydrograph into its three distinct stages; inflow, delayed inflow and groundwater infiltration.The separation approach relies on identifying the distinct difference in the time frame of each RDII stage.The discontinuities in the slope of the flow hydrograph reveal the peak and terminal recession times for each of the three stages.These stages are then simulated in SWMM using the RTK unit hydrograph method.A single set of RTK parameters for each RDII stage is calculated using rainfall volume, duration and start and peak times, and the time for flow routing through the collection system network.A combination of precipitation, depression storage recovery rate, and the calculated single set of the RTK parameters can then be used to calculate RDII response in continuous simulation of hydrology and hydraulics models.This chapter details a secondary, but essential, step towards calculating a single set of RDII parameters for continuous calibration.The primary step (Gheith, 2010) was a procedure for calculating the total R value that is only a function of the internal factors affecting the RDII process.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".