Storage measures as compensatory techniques for urban lowlands flood control
Bibliographic record
Abstract
Urban fl ood problems are being aggravated in growing cities. The process of urbanisation generally tends to supress natural retention areas, removing natural vegetation and producing large impervious areas. Besides, the traditional urban drainage approach, comprising mainly canalisation measures, showed to be potentially unsustainable, tending to transfer fl oods to downstream. Gradually, in the past recent years, this practice has been complemented or replaced by new concepts considering the use of distributed interventions to approximately recover fl ow patterns prior to the urbanisation. In Brazilian great cities, drainage systems' design started to incorporate the use of the so-called Compensatory Techniques, which aim to compensate the effects for the urbanisation process over the water cycle. In this context, detention and retention reservoirs have been conceived as potential adequate solutions. Departing from a Municipality proposition for a new urban development in Guerengu River catchment, west zone of Rio de Janeiro, and considering the concept of compensatory techniques applied to the urban drainage, an alternative confi guration for the drainage system is proposed in the catchment scale, and compared with the critical present situation. In this alternative proposal, besides the Municipality actions, a complementary set of storage measures distributed along the riverine areas was considered. Additionally, local measures, composing multifunctional landscapes on the microdrainage scale, were also introduced to face local inundation problems. The scenarios assessment was supported by mathematical modelling. Modelling results showed that, at present situation, great part of the catchment suffers from fl ooding, with water depths that usually range from 0.15 m to 1.50 m. In critical areas, fl ooding may surpass the 1.50 m high level. The introduction of the compensatory techniques was capable of signifi cantly changing this situation. However, for a more effective result, land use planning must also be addressed in the context of fl ood control.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".