Meta-Analysis of Storm Water Impacts in Urbanized Cities Including Runoff Control and Mitigation Strategies
Bibliographic record
Abstract
The rate of urbanization has been impacted by global economic growth. A strong economy results in more people moving to already crowded urban centers to take advantage of increased employment opportunities often resulting in sprawling of the urban area. More natural land resources are being exploited to accommodate these anthropogenic activities. Subsequently, numerous natural land resources such as green areas or porous soil, which are less flood-prone and more permeable are being converted into buildings, parking lots, roads and underground utilities that are less permeable to storm water runoff from rain events. With the diminishing of the natural landscape that can drain storm water during a rainfall event, urban underground drainage systems are being designed and built to tackle the excess runoff resulting from urbanization. However, the rapid pace of urbanization has profoundly affected the formation of urban runoff thus resulting in the existing underground drainage system being unable to handle current flow conditions. This paper discusses storm water impacts in urbanized areas globally by reviewing historical storm water events and mitigation strategies accompanied with runoff reduction performance that are considered simultaneously for the purpose of relieving the stress on underground drainage systems. It was found that the stormwater impact on ten selected typical urban areas were enormously destructive followed by billions of direct economy loss, fatalities, damaged properties and residents’ relocations. Furthermore, the meta-analysis of selected six runoff mitigation methods indicated that the average runoff reduction percent ranged from 43% to 61% under different rain events in various installed sites across different event years.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.043 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".