Distributed urban storm water modeling within GIS integrating analytical probabilistic hydrologic models and remote sensing image analyses
Bibliographic record
Abstract
Analytical probabilistic hydrologic models (APMs) are computationally efficient producing validated storm water outputs comparable to continuous simulation for storm water planning level analyses. To date, APMs have been run as spatially lumped or semi-distributed models relying upon calibrated and spatially averaged system state variable inputs/parameters limiting model system representation and ultimately impacting model uncertainty. Here, APMs are integrated within Geographic Information Systems (GIS) and remote sensing image analyses (RSIA) deriving a planning-level distributed model under refined model system representation. The hypothesis is refinements alone, foregoing model calibration, will produce trial average annual storm water runoff volume estimates comparable to former research estimates (employing calibration) demonstrating the benefits of improved APM system representation and detail. To test the hypothesis three key system state variables – sewershed area, runoff coefficients and depression storage – are digitally extracted in GIS and RSIA through: automated delineation upon a digitally inscribed digital elevation model; unsupervised classification of an orthophotograph; and a slope-based expression, respectively. The parameters are spatially-distributed as continuous raster data layers and integrated with an APM. Spatially-distributed trial runoff volumes are within a range of 4–29% of earlier lumped/semi-distributed research estimates validating the hypothesis that further detail and physically-explicit representations of model systems improve simulation results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".