Coordinated efforts in tsunami hazard and risk analyses in Europe and link to the Global Tsunami Model network initiative
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,044 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».