Assessment of Stormwater Drainage Network to Mitigate Urban Flooding Using GIS Compatible PCSWMM Model
Bibliographic record
Abstract
Urban flooding has caused immense damage to the society. Flooding in urban areas mostly occurs due to increased urbanization, low rate of infiltration, and poor infrastructure for stormwater drainage network. In the present study, PCSWMM model is used for modelling the stormwater drainage network for the southern part of Delhi, the capital city of India, which is developed by Computational Hydraulics International (CHI), Canada, and it is GIS compatible which makes this software more efficient. All the collected field survey details data of the stormwater drains were incorporated in ArcMap 10.2 and then imported in PCSWMM to develop a hydrology-hydraulic model for surface runoff. The simulated results of the model were further calibrated and validated with the flooding locations data obtained from the Delhi Traffic Police Department. The simulated results were closely matching with the observed data and thus, can to use for designing stormwater drainage network.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".