Inventory models for regional scale natural hazards risk assessment
Notice bibliographique
Résumé
This document presents the results of research efforts aimed to develop inventory models of the demography and general building stock for urban centers and rural communities across eastern Canada. The inventory models are intended for use in the rapid seismic risk assessment tool ER2 (for Rapid Risk Evaluation). Research and development of the inventory models were carried out jointly by the University of Ottawa and École de technologie supérieure (ETS) Montréal. These were part of the larger Prompt Evaluation of Seismic Risk project (PESR CSSP-2016-CP-2283), led by National Resources Canada (NRCan) for the Canadian Safety and Security Program (CSSP) managed by Defence Research and Development Canada (DRDC) Centre for Security Science (CSS) and Public Safety Canada. The ER2 tool informs the public safety community and emergency management decision makers with information on various aspects of seismic risk. It consists of two software components for two distinct types of use. The first focusses on near real-time risk analyses following a major earthquake event, while the second component supports various risk assessment initiatives for scenario-based risk analyses. The inventory models were developed across a study area extending from the Greater Toronto Area (Ontario) to Quebec City (Quebec). The study boundary encompasses approximately 123,455 km2, 6,398 census units (census tracts and dissemination areas) and over 4.2 million buildings. Detailed inventories for building types and occupancy classes were conducted for 12 municipalities ranging from rural communities to large urban centres in both Ontario and Quebec. Since these inventories cover only about 0.6% of the study area (km2) or 4.8% of the total number of buildings, a procedure had to be developed to extrapolate representative building information from the detailed inventories to be applied across the remaining census units in the study area. First, the procedure started with an estimation on the number of buildings for each census unit based on available geospatial datasets; demographic information was also collected. Second, each census unit was identified by an IoX class code that accounts its population (by size and as populated or non-populated), land use (as residential or commercial, and related densification) and average age (before or after 1960). All census units within the study area was represented in total by 45 different IoX classes. Third, the distribution of building characteristics within the detailed inventory were summarized by IoX code which provide a reasonable representation of the actual construction practices. Within the inventoried buildings, around 94% are residential buildings, 93% are wood constructions and around 74% were build after 1960. Distribution of building characteristics differ depending on the size of the community, its main land use and the average year of construction. In order to estimate the potential economic losses from earthquake scenarios, average square footage, and replacement and content values in dollar terms were associated for various occupancy classes in each default IoX class. To further estimate potential social losses (injuries, fatalities, shelter needs), demography distribution models were built considering three common times of the day: 2am (nighttime), 2pm (daytime) and 5pm (commuting time), accompanied with respective residential, working and commuting population estimations. A two-tiered approach was used to address population distributions. These tiers represent a 'rural' model where it is assumed that the 'workers' work within their census unit and the 'urban' model where it is assumed that most of the 'workers' commute to another census unit.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».