Comprehensive ArcGIS-Based Urban Drainage Modeling for Decision Support
Bibliographic record
Abstract
Computer simulation models of urban drainage systems represent the most effective and viable means for evaluating system response to various management strategies.To be effective, these models require extensive spatial utility infrastructure data readily available from a Geographic Information System (GIS).Indeed, a utility's database contains the objects that make up the network and information about these objects.The GIS is used to link this information to the digital map.Used as a spatial database, GIS can greatly assist in various modeling and analysis applications through the development of automated tools for constructing and maintaining reliable network models of urban drainage systems.This chapter presents a comprehensive GIS-based decision support system that integrates several technologies for use in the effective management of urban stormwater collection systems.It explicitly integrates ESRI ArcGIS geospatial model with advanced hydrologic, hydraulic, and water quality simulation algorithms based on the USEPA SWMM5 urban drainage network solver, global optimization techniques based on fast messy genetic algorithms for calibration and design, automated dry weather flow generation and allocation, and automated subcatchment delineation and parameter extraction to address every facet of urban drainage infrastructure management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".