Alternative storm-water management scenarios for developing countries in urban contexts
Bibliographic record
Abstract
The rapid expansion of cities has tremendously increased the storm water volume, which frequently causes local flooding. Conventional drainage systems are often unable to sustain these run-off volumes. This has caused an appreciable rise in the implementation of water-sensitive urban design techniques to manage the storm water. Incorporation of sustainable strategies becomes even more critical for developing countries, where there is continued demand for expanding urban water infrastructure. This study presents innovative alternate scenarios for managing storm water sustainably for urban areas in developing countries. The scenarios consist of combinations of multiple alternate measures provided in a decentralised manner. The study also seeks to evaluate the hydrologic performance of these scenarios. A simple methodology, based on the Natural Resources Conservation Service curve number method and Arc-CN Runoff tool, is developed for analysing hydrologic benefits and is applied to a sub-catchment in Pune City, India, to demonstrate its utility for urban areas. The results clearly indicate the effectiveness of implementing these scenarios in improving urban hydrological response. The current study is intended to appraise urban local bodies and stakeholders regarding sustainable, decentralised ways of managing storm water and their potential hydrological impacts at the watershed scale.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".