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Record W2917091322 · doi:10.1111/jfr3.12263

Sixth International Conference on Flood Management (<scp>ICFM6</scp>): Floods in a changing environment, part 1

2016· article· en· W2917091322 on OpenAlexaff
Slobodan P. Simonović, André Schardong

Bibliographic record

VenueJournal of Flood Risk Management · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsWestern University
Fundersnot available
KeywordsFlooding (psychology)Flood mythRainwater harvestingEnvironmental planningGeographyPopulationUrbanizationFloodplainUrban infrastructureWater resource managementEnvironmental scienceUrban planningCivil engineeringEnvironmental healthEconomic growthCartographyEngineeringArchaeology

Abstract

fetched live from OpenAlex

The ICFM6 (Sao Paulo 2014) conference devoted considerable attention to urban flooding (the host city Sao Paulo being an excellent example). This special issue offers a selected number of papers out of 314 presented at the conference (110 oral and 204 poster presentations) that attracted over 230 participants from 32 countries. The current challenges to managing urban flooding and examination of potential solutions are presented in this first part of the special issue. The risk of flooding is defined as a function of both the probability of a flood happening and its impact. In urban areas, the impact of flooding is very high because the areas affected are densely populated and contain vital infrastructure. Continuing development in flood-prone urban areas increases the risk. In municipalities across the world, several factors have resulted in increased flood damage and there is an indication that the situation will likely get worse in the near future. The sources of concern include: (i) Social and demographic change. Global population has been increasing and there has generally been more exposure of people to floods. There has also been a growing inequality in the difference between poor and wealthy members of society and the poor are more vulnerable. (ii) Changes to built environment. The density of buildings has been growing and most of the water infrastructure is old and its condition is unknown. As new developments cover previously permeable ground, the amount of rainwater running off the surface into drains and sewers increases dramatically. The proportion of impermeable ground in existing developments is increasing too. (iii) Changes to the physical environment, specifically climate change. (iv) Changes in flood management policy. If the general message can be presented, the publication included in this special issue point out that multiple sources of flood risk must be considered together. The separate sources of coastal, river, rainfall, and groundwater flooding often occur together and the combined impact can be greater than the sum of each alone. Each source of flooding has different frequencies and so the consideration of risk as ‘Probability × Consequence’ is useful. However, this must include the need to focus on low-probability, high-consequence extreme events that may destroy the ability of a particular society to recover, i.e. an existential risk. Flood response features often have benefits beyond flood risk, i.e. environmental and societal benefits. It needs to be ensured that these are taken into account in any cost–benefit analysis. The wider economic impacts of flooding, sometimes beyond the geographical area where they occur, should be taken into account in any cost–benefit analysis.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.011
GPT teacher head0.231
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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