The potential of two types of urban flooding to cause material damages in Lisbon, Portugal
Bibliographic record
Abstract
The incapacity to cope with heavy rainfall events leads to flooding in urban areas.Urban flooding is usually divided into pluvial flooding, sewer flooding, among others.Determining which of these types was responsible to cause a flooding occurrence is, in most cases, an error-prone task, because it can be triggered by more than one of them and there is frequently an overlap in their extent.Given these problems, a classification based on the hydro-geomorphological features was applied to Lisbon, in which the relief and the ancient floodplains are still crucial factors in the city's current overland flow behavior.Two types of flooding are proposed: flooding related to natural drainage network (FREN) and flooding unrelated to natural drainage network (FUNN).In order to determine the material damages associated to FREN and FUNN and their spatial distribution, an insurance flooding database (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) was used.Through the accurate location of the APS (the Portuguese Association of Insurers) database flooding records triggered by rainfall and the reconstruction of the Lisbon's natural drainage network, it was possible to define which type of flooding caused each reported claim.There are different spatial patterns in Lisbon concerning FREN and FUNN.FREN occurs along the valley bottoms, while FUNN has a more scattered spatial pattern, meaning that FUNN can occur where there are overland flow difficulties.53% of the claims were caused by FUNN; however, higher payouts are associated with FREN (58%).Resorting only to the claims recorded during an extreme rainfall event (in 18 February 2008), the weight of FREN is even more pronounced with 58% of the claims and 71% of the payouts.This proves the higher FREN's potential to cause material damages when compared to FUNN's.This knowledge can be applied to the flooding mitigation or adaptation measures to be included in urban planning.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".