Coordinated efforts in tsunami hazard and risk analyses in Europe and link to the Global Tsunami Model network initiative
Bibliographic record
Abstract
The tsunami disasters of 2004 in the Indian Ocean and of 2011 along the Tohoku coast of Japan revealed severe gaps between the anticipated risk and consequences, with resulting loss of life and property. A similar observation is also relevant for the smaller, yet disastrous, tsunamis with unusual source characteristics such as the recent events in Palu Bay and Sunda Strait in 2018. The severe consequences were underestimated in part due to the lack of rigorous and accepted hazard analysis methods and large uncertainty in forecasting the tsunami sources. Population response to small recent tsunamis in the Mediterranean also revealed a lack of preparedness and awareness. While there is no absolute protection against large tsunamis, a more accurate analysis of the potential risk can help to minimize losses. The tsunami community has made significant progress in understanding tsunami hazard from seismic sources. However, this is only part of the inputs needed to effectively manage tsunami risk, which should be understood more holistically, including non-seismic sources, vulnerability in different dimensions and the overall societal effects, in addition to its interaction with other hazards and cascading effects. Moreover, higher standards need to be achieved to manage and quantify uncertainty, which govern our basis for tsunami risk decision making. Hence, a collective community effort is needed to effectively handle all these challenges across disciplines and trades, from researchers to stakeholders. To coordinate and streamline these activities and make progress towards implementing the Sendai Framework of Disaster Risk Reduction (SFDRR) the Global Tsunami Model network (GTM) was initiated in 2015 towards enhancing our understanding of tsunami hazard and risk from a local to global scale. Here, we focus on coordinated European efforts, sharing the same goals as GTM, towards improving standards and best practices for tsunami risk reduction. The networking initiative, AGITHAR (Accelerating Global science In Tsunami HAzard and Risk Analysis), is a European COST Action, aims to assess, benchmark, improve, and document methods to analyse tsunami hazard and risk, understand and communicate the uncertainty involved, and interact with stakeholders in order to understand the societal needs and thus contribute to their effort to minimize losses. In this presentation, we provide an overview of the suite of methodologies used for tsunami hazard and risk analysis, review state of the art in global tsunami hazard and risk analysis, dating back to results from the Global Risk Model in 2015, and highlight possible gaps and challenges. We further discuss how AGITHAR and GTM will address how to tackle these challenges, and finally, discuss how global and regional structures such as the European Plate Observing System (EPOS) and the UNDRR Global Risk Assessment Framework (GRAF) can facilitate and mutually benefit towards an integrated framework of services aiding improved understanding of multiple hazards.
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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 | 0.044 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".