Managing floods using sustainable infrastructure, a case study
Bibliographic record
Abstract
Floods occur in the Al Rawdah neighbourhood in Irbid city due mainly to excessive precipitation, the abundance of impervious surfaces, and shortages in the current drainage capacity. Global warming and urbanisation have also increased flood risks and severity. Therefore, green infrastructure (GI) was proposed to alleviate flood risks and to benefit from heavy stormwater events. The current study analysed the urban floods in the study area and compared the hydraulic performance of the existing network, the grey network, and the GI flood management systems. The ability of these systems in managing runoff, inflow, flooding, and storage capacity in different storm scenarios was evaluated. Several software, including Storm and Sanitary Analysis and Global Mapper, were used. Results show that the GI system significantly outperforms both the existing and grey networks. In terms of mitigation of flooding, runoff reduction, and storage capacity, the green system was more effective than the grey systems, particularly during severe storms with extended return periods. The green system was more resilient to flooding due to its capacity to store and delay runoff flow. It also performed effectively in capturing runoff. Economically, the green system is more cost-effective to construct and maintain, with a higher benefit-cost ratio.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".