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Enregistrement W6966946410 · doi:10.4224/40002986

Flood damage to critical infrastructure

2022· report· en· W6966946410 sur OpenAlexafffundvenueabout

Notice bibliographique

RevueNPARC · 2022
Typereport
Langueen
Domaine
Thématique
Établissements canadiensNational Research Council CanadaGovernment of Canada
Organismes subventionnairesNational Research Council CanadaNatural Resources CanadaEnvironment and Climate Change CanadaPublic Safety Canada
Mots-clésCritical infrastructureDamagesFlood mythResilience (materials science)Vulnerability (computing)HazardWork (physics)Natural hazardClimate change

Résumé

récupéré en direct d'OpenAlex

This report provides a review of the tools and metrics available for flood hazard mappers, planners, engineers and scientists for describing the vulnerability of critical infrastructure in Canada when exposed to coastal and riverine flooding. This research was undertaken for Public Safety Canada and in collaboration with Natural Resources Canada (NRCan) and Environment and Climate Change Canada (ECCC). Consultation with these research partners identified a need for more science-based tools and methods to estimate the resistance and resilience of critical infrastructure to flood hazards, beyond post flood-damage inspection surveys. The critical infrastructure that has been identified for this work includes roads, rail, pipelines, telecommunication lines, water supplies, sewage treatment facilities, fire stations, hospitals, police stations, emergency medical services (EMS), electricity production and distribution, other transportation infrastructure, hotels, schools and community centres. The greatest amount of available information on methods for assessing flood damage pertains to buildings. Canadian stage-damage functions exist for the structure and contents of Canadian buildings identified as critical infrastructure. Further information is also available from sources within the United States that could allow for the incorporation of waves and currents into the building stage-damage functions. No North American-based stage-damage functions were found for predicting flood damages to road and rail in the available literature. Stage-damage functions have been provided based on data from Asia, which has been found to be similar to data from Europe. However, these available empirical stage-damage functions are based on post-flood surveys and therefore are inherently specific to local conditions and infrastructure design/construction methods, which may not reflect Canadian settings. The functions are broad, they do not account for the different types of infrastructure which make up these systems such as tunnels and bridges where most flood damage occurs, they are simply based on length of road or railway. For other types of critical infrastructure, stage-damage functions were found in studies from the United States. Those functions were predominantly based on identifying the height of critical components susceptible to damage from flooding. As such, these functions incorporated an element of subjectivity and lack validation in either controlled or uncontrolled environments. No stage-damage functions were found for communications systems. The elements that comprise communications systems have been identified and most elements could be assessed in a manner similar to the other infrastructure systems provided in this report. One exception is utility poles. Utility poles were not identified as critical elements in either electrical or communications systems of existing stage-damage functions and these features were highlighted by our project partners as one of the gaps in current flood hazard mapping and planning. Recently published work should provide the theoretical basis for the development of analytical stage-damage functions for utility poles exposed to flooding. Recommendations are made for potential future research directions to add data-driven methods for estimating damages to critical infrastructure which are exposed to flood hazards in an effort to Flood Damage to Critical Infrastructure improve and add resolution to existing stage-damage functions, as well as fill any gaps by developing new stage-damage functions for critical infrastructure in Canada.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,038

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0040,002
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0110,001

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.

Tête enseignante Opus0,027
Tête enseignante GPT0,335
Écart entre enseignants0,308 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission4
Résumé présentoui

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