Construction Cost-Based Effectiveness Analysis of Green and Grey Infrastructure in Controlling Flood Inundation: A Case Study
Bibliographic record
Abstract
Global climate change and rapid urbanization have increased the frequency of urban flooding.In this work, SWMM was used to simulate the implementation of low impact development (LID) practices, drainage network reconstruction, and detention tanks to evaluate potential reductions in flood severity in the TQ subdistrict of SH city, China.Two assessment indexes, construction costs and construction effectiveness, were used to evaluate model results for three scenarios.Construction effectiveness was quantified by reductions in total flood volume and peak flow rate.Results show that LID practices such as permeable pavement and sunken green space mitigate the flooding by reducing total flood volume by 8.6% and decreasing surface runoff by 7.9%, at a cost of $0.34 million.Reconstructing the drainage network reduced total flood volume by 38.4%, which is less than the 46.6% given by detention storage construction.However, the peak flow for drainage network reconstruction decreased by 46.6% and peak flow for detention construction was reduced by 50.1%.Despite the lower peak flows, three nodes experienced flood durations longer than the general guideline of 1 h for network reconstruction, while only two nodes did so with detention tanks.Thus, detention tanks are more effective in reducing the effects of flooding than drainage network reconstruction.Detention tanks are also more cost effective, costing $1.59 million, while network reconstruction cost $1.74 million.While this study considers flood risk reduc-tion only in regard to economics and effectiveness, the benefits extend to the environment, ecology and human populations.Consequently, this paper can be used as a guide for planners and engineers who make flood mitigation decisions in using these techniques for balancing construction costs and effectiveness.This study should be meaningful for urban stormwater system design, planning, and construction in other southeastern cities of China with high water coverage ratios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".