Enhancing Urban Drainage in Coastal Cities: A Simulation-Based Assessment of Nature-Based Solutions for Climate Resilience
Bibliographic record
Abstract
Climate change is now fully expressed through extreme rainfall and sea-level rise, and it is a major threat to coastal cities globally. Additionally, the exhaustion of urbanization makes the situation even more difficult. Conventional drainage systems are overburdened by the rising demand; thus, Nature-Based Solutions offer a way to build systemic resilience which is characterized by the restoration of natural hydrological functions. The main objective of this paper is to analyze the role of integrated NBS in the improvement of hydraulic performance in the drainage of tropical coastal cities. In this regard, we conduct a systematic literature review alongside scenario-based simulations using the Storm Water Management Model (SWMM) which is supplied with synthetic data that reflects a typical tropical coastal city. The findings suggest that a distributed network of bioswales, rain gardens, and permeable pavements may decrease the peak discharge and total runoff volume by 28.8% and 29.0% respectively, these changes involving to a great extent infiltration enhancement and time to peak delay. Hence, this research provides a quantifiable, conceptual basis that is applicable to the field of urban planners and engineers as a means of warranting the trend of NBS as an essential part of the living adaptations in jeopardized coastal urban zones.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".