Runtime Comparisons between SWMM 4 and SWMM 5 using Continuous Simulation Model Networks
Bibliographic record
Abstract
The Stormwater Management Model (SWMM) is a dynamic rainfall-runoff model used for continuous simulation of runoff quantity and quality.SWMM has recently been redeveloped under a Cooperative Research and Development Agreement (CRADA) between CDM Inc. and the U.S. Environmental Protection Agency (USEPA).One of the purposes of the CRADA is to improve the numerical stability of the model without changing the fundamental characteristics of the EXTRAN solution or sacrificing the efficiency of its performance.Extensive QA/QC testing was performed as part of the CRADA to ensure consistency between the SWMM4 and SWMM5 solutions.(Dickinson et al., 2004;Rossman et al., 2003;Schade, 2002;Chan et al., 2003).Another aspect of QA/QC testing was to compare simulation runtimes between SWMM4 and SWMM5.This chapter presents the computational speed comparison between SWMM4 (Huber & Dickinson, 1988) and SWMM5 (Rossman et al., 2003).Various SWMM4 historical applications were used and runtimes of SWMM4 and SWMM5 were plotted and compared by different calculation methods.This chapter also describes the approach used to test the simulation speed in SWMM4 and SWMM5.All models were run on the same computer workstation for fairness in the comparison.The test computer was equipped with a commonly available Pentium® P4-1.6 GHz processor with 256Mb RAM.The SWMM4 models were run with SWMM499 and the new SWMM5 (version Beta E).To ensure data consistency, the historical SWMM4 applications were translated to SWMM5 input format, using the converter software distributed with the SWMM5 package.Results generated
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".