Simulation of Sustainable Stormwater Drainage Systems in Kuergeng Town Using EPASWMM and Remote Sensing Data
Bibliographic record
Abstract
Impermeable behaviour of manmade structures and urbanization make surface runoff and flooding more likely, especially in low-lying areas with poor drainage systems. This study aimed to simulate the performance of a sustainable stormwater drainage system designed in Kuergeng Town, Gambella, Ethiopia using EPA software (SWMM 5.1) and remote sensing data. A 30-meter Digital Elevation Model (DEM) and 30-year daily and monthly temperature and precipitation data from the National Aeronautics and Space Administration (NASA), land use/land cover data, and hydrological soil group data were among the important datasets. Using the Thiessen polygon methodology, the total area of 1,280.18 hectares was delineated and divided into six sub-catchments using ESRI software ArcGIS10.3. A 60-year return period obtained from plotting position methods and rainfall depth of 62.15 mm were considered for estimation of the peak runoff (30.31 m³/s) by the Soil Conservation Service Curve Number (SCS-CN) method. The peak runoff of 33.51 m³/s was obtained by simulating the system using SWMM v5.1, which resulted in 10.59% higher than the existing peak runoff. The study concluded that the drainage network should be redesigned considering a safety factor of 10.59% as a correction factor and establishing rain garden in the flood prone area in the town. The results also highlighted the importance of remote sensing data for sustainable stormwater management, especially in the areas with limited onsite data and economic vulnerability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".