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Record W2766469605 · doi:10.1080/19475705.2017.1388854

<i>FloodRisk</i>: a collaborative, free and open-source software for flood risk analysis

2017· article· en· W2766469605 on OpenAlexaff
Raffaele Albano, Leonardo Mancusi, Aurelia Sole, Jan Adamowski

Bibliographic record

VenueGeomatics Natural Hazards and Risk · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsMcGill University
FundersBasilicata Regional authorityMinistero dell’Istruzione, dell’Università e della Ricerca
KeywordsFlood mythContext (archaeology)Risk-based testingRisk analysis (engineering)Transparency (behavior)Environmental resource managementRisk managementSustainable developmentFlood mitigationStakeholder engagementComputer scienceBusinessEnvironmental planningSoftwareSoftware developmentGeographyEnvironmental sciencePolitical science

Abstract

fetched live from OpenAlex

The European ‘Floods Directive’ 2007/60/EC focuses on the development of flood risk maps and management plans on the basis of the most appropriate and advanced tools. This pushed a paradigm shift for moving to sustainable development through processes of stakeholder engagement to improve the efficiency and transparency of decision processes. In this context, this research project developed a free and open-source GIS software, called FloodRisk, to operatively support stakeholders in their compliance with risk map delineation and the management of current and future flood risk based on their needs for multi-purpose applications. In this paper, a high-resolution impact assessment framework based on 2D inundation modelling with different return periods was used, as input, within the FloodRisk model to reconstruct the socio-economic damages based on a case study showing how structural and non-structural measures can significantly decrease the cost of floods for households. The sensitivity of the FloodRisk model was also examined and it was found to be highly dependent on the selection of damage functions and the economic values of the exposed assets.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.254
Teacher spread0.249 · 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; a candidate call from one teacher head, not a consensus.

Study designObservational
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

Citations44
Published2017
Admission routes1
Has abstractyes

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