Considering uncertainty in the Quantitative Risk Analysis process to inform decision-making for landslide risk mitigation strategies
Bibliographic record
Abstract
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.
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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 | 0.031 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".