Analysis of Urban Drainage Simulations of an Immensely Urbanized Watershed using the Pcswmm Model
Bibliographic record
Abstract
Flooding has caused immense damage to the people as well as to the property. Flooding in urban areas mostly occurs due to increased urbanization, low rate of infiltration and poor infrastructure for stormwater drainage network. Stormwater Management Model (SWMM) is found to be very dynamic hydrology-hydraulic water quality simulation model for modeling of the urban stormwater drainage network. In the present study, PCSWMM model is used for modeling the stormwater drainage network for the southern part of Delhi, the capital city of India. PCSWMM is developed by Computational Hydraulics International (CHI), Canada. PCSWMM uses the same SWMM engine for the modeling work; the only advantage is that it is GIS compatible software which makes this model more efficient. The model required following input information for simulation, i.e., land-use for calculating impervious and previous area, soil type, 15-minute interval precipitation data, temperature, humidity, and three-dimension cross-sectional geometry of the existing drainage network. A field survey was carried out for data collection, and in the process, it was found that most of the storm-water drains are choked, have improper flow gradient 370 or damaged. All the collected field details of the storm-water drains were incorporated in ArcMap 10.1 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 available flooding locations data obtained from the Delhi Traffic Police Department. The simulated results were in close agreement with the observed flooding locations. Thus PCSWMM model can be applied to any urban/rural areas for designing stormwater drains or 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) | 0.000 | 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 | 0.000 |
| 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".