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David O Prevatt

Dados Biográficos

ID9720380
NOMEDavid O Prevatt
PRENOMESDavid
SOBRENOMEO Prevatt
ASSINATURAPREVATT D O
AFILIAÇÕESUniversity of Florida
ORCID0000-0001-9365-9776
VERIFICADOSim
TOTAL DE OBRAS3
TOTAL DE CITAÇÕES0
TOTAL COMO AUTOR3
TOTAL COMO EDITOR0
PRIMEIRO ANO DE PUBLICAÇÃO2024
ANO MAIS RECENTE DE PUBLICAÇÃO2024
ÍNDICE H0
  • Empirical hurricane fragility assessment of elevated and slab-on-grade residential houses

    Open Access•Haitham A Ibrahim, Amal Elawady et al.•ARTICLE•International Journal of Disaster…•2024

  • Predictive modeling of severe weather impact on individuals and populations using Machine Learning

    Open Access•Jordi Iglesias, Ildefonso Cuesta et al.•ARTICLE•International Journal of Disaster…•2024

    In this work, Machine Learning (ML) techniques are used to develop tools capable of accurately predicting the impact of severe weather events. We use readily accessible predictors, including daily meteorological data, basic demographics, geographic and terrain features, along with the number of daily meteorological incidents reported to the emergency services. The model was built using dissaggregated data from the 947 municipalities from the regi…

  • A Unified Multievent Windstorm Performance Testbed for Single-Family Residential Buildings

    Open Access•David B Roueche, Jordan O Nakayama et al.•ARTICLE•Natural Hazards Review•2024

    Recent advancements in technology and infrastructure have greatly improved the capabilities of the natural hazards community to collect robust samples of building performance following hazard events and make them available to the research community for broad reuse purposes. Yet, there lacks standardized, open access data sets that combine reconnaissance data from multiple individual hazard events into unified, living testbeds that can grow throug…

Sem obras proeminentes nesta página.

  • Empirical hurricane fragility assessment of elevated and slab-on-grade residential houses

    Open Access•Haitham A Ibrahim, Amal Elawady et al.•ARTICLE•International Journal of Disaster…•2024

  • Predictive modeling of severe weather impact on individuals and populations using Machine Learning

    Open Access•Jordi Iglesias, Ildefonso Cuesta et al.•ARTICLE•International Journal of Disaster…•2024

    In this work, Machine Learning (ML) techniques are used to develop tools capable of accurately predicting the impact of severe weather events. We use readily accessible predictors, including daily meteorological data, basic demographics, geographic and terrain features, along with the number of daily meteorological incidents reported to the emergency services. The model was built using dissaggregated data from the 947 municipalities from the regi…

  • A Unified Multievent Windstorm Performance Testbed for Single-Family Residential Buildings

    Open Access•David B Roueche, Jordan O Nakayama et al.•ARTICLE•Natural Hazards Review•2024

    Recent advancements in technology and infrastructure have greatly improved the capabilities of the natural hazards community to collect robust samples of building performance following hazard events and make them available to the research community for broad reuse purposes. Yet, there lacks standardized, open access data sets that combine reconnaissance data from multiple individual hazard events into unified, living testbeds that can grow throug…

Engineering (3 obras) · Environmental Science (3 obras) · Tropical and Extratropical Cyclones Research (3 obras) · Computer Science (2 obras) · Fragility (2 obras) · Geography (2 obras) · Meteorological Phenomena and Simulations (2 obras) · Meteorology (2 obras) · Wind and Air Flow Studies (2 obras) · Chemistry (1 obra)

Ethnos_APP • Projeto Open Source • Licença MIT • Frontend v2.0.0 • Privacidade e Cookies • Documentação da API: api.ethnos.app/docs • Código da API: GitHub • DOI: 10.5281/zenodo.17049435 • Código do Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae