Living-with-Water: a Comprehensive Design Proposal to Build Flood Resilience in the Roncador River Region, Brazil
Bibliographic record
Abstract
Informal settlements on riverbanks in impoverished Brazilian peripheries have been increasingly suffering from more intense annual urban floods, as in the Roncador River region in Duque de Caxias City. This article proposes a comprehensive solution for flood risk reduction (FRR) through an integrated approach in design. By recognizing water as an ally, this study connects a system of green areas along the river corridor and within the urban fabric with amphibious evolutionary housing as an adaptive solution that protects houses from flood damage. As a low-impact intervention, it prioritizes nature-based strategies and local community practices, fostering local economies to fight gentrification and contributing to building a more equitable future. The methodology identifies the region’s problems and opportunities, followed by a literature review on FRR solutions and incremental housing design strategies. Lastly, two sites were selected to propose the design intervention. As a result, the design applies adaptability strategies on different scales, accepting floods, allowing transformations, and adapting to the local context. The proposed green areas’ system on the watershed scale increases soil permeability and water storage and reduces stormwater runoff. On the housing scale, residents are provided with a low-cost, flexible, and amphibious starter house that is half the potential final house area on safe nearby lands. The design solution promotes economic benefits, as implementing a network of parks improves the land value and generates local sources of employment. The project’s innovation is combining incremental design strategies with amphibious architecture to offer good quality and affordable housing that adapts to floods, empowering marginalized communities to thrive in healthier riverscapes. In addition, this solution could be applied to improve the livelihood of other flood-prone communities in similar informal contexts.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".