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Record W4321495577 · doi:10.5194/egusphere-egu23-1757

Considering uncertainty in the Quantitative Risk Analysis process to inform decision-making for landslide risk mitigation strategies

2023· preprint· en· W4321495577 on OpenAlexaffabout
Renato Macciotta

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsMacEwan UniversityUniversity of Alberta
Fundersnot available
KeywordsRisk analysis (engineering)Process safetyRisk assessmentRisk managementVulnerability (computing)Process (computing)Environmental resource managementComputer sciencePopulationHazardEnvironmental scienceEngineeringBusinessWork in processOperations managementComputer security

Abstract

fetched live from OpenAlex

The adoption of quantitative risk assessments (QRA) for land-slide management decision-making has increased over the last few decades, particularly when projects threaten sensitive built environments and heritage sites. The QRA process provides a quantitative estimate of the level of risk that can then be evaluated against adopted criteria for decision-making purposes regarding the need for prevention and mitigation. Although the QRA process provides for considerations of uncertainty in landslide hazard (occurrence probability, volumes, velocities, runout distances, etc.) and consequence (e.g. quantity and vulnerability of exposed population and infrastructure); The uncertainty associated with quantification in the QRA process is seldom understood or quantified. This presentation shares the outcome of a research project where the uncertainties associated with the QRA process were quantified in order to gain an understanding of the reliability in landslide QRA. The results are evaluated in terms of typical ranges within common risk tolerability criteria. The knowledge gained on this project was used to develop a simplified approach to consider uncertainty in QRA for practical purposes, which is illustrated for a section of highway exposed to rock fall hazards in Canmore, Alberta, Canada. The QRA was selected to inform decision-making for the selection of rock fall protection strategies at a location where environmental concerns, tourism activities, and economic activities are of significant value for the public. This significantly increased the complexity of the decision-making process, and therefore required a robust, clear approach for balancing public socio-economic expectations and safety. In the QRA process, uncertainty was associated with hazard and consequence quantification. The work elicited the plausible ranges for the input variables for risk calculation. The expected and the range in risk were calculated for the current conditions and considering the implementation of the mitigation option. The individual risk to highway users was considered low because of the limited exposure of any particular individual. The calculated current total risk (probability of fatality) was 2.9 × 10−4 with a plausible range between 2.0 × 10−5 and 5.5 × 10−3. The residual total risk considering implementation of the slope protection was calculated between 9.0 × 10−4 and 2.9 × 10−6, with an expected value of 4.5 × 10−5.The risk levels considering implementation of the mitigation options were evaluated against criteria previously used in Canada. These were considered an adequate balance between project costs, public safety, environmental concerns, tourism, and economic activities.

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 imitation

Not 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.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.031
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.059
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.004
Scholarly communication0.0090.007
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.333
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2023
Admission routes2
Has abstractyes

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