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    Hurricane Risk Assessment for Residential Buildings in the Southeastern US Coastal Region in Changing Climate Conditions Using Artificial Neural Networks

    Source: Natural Hazards Review:;2020:;Volume ( 021 ):;issue: 003
    Author:
    Chi-Ying Lin
    ,
    Eun Jeong Cha
    DOI: 10.1061/(ASCE)NH.1527-6996.0000390
    Publisher: ASCE
    Abstract: Hurricanes cause severe damage along US coastal regions, which may be aggravated in changing climate conditions. This paper investigates the impact of climate change on the hurricane risk for residential buildings in the southeastern US coastal region. To understand hurricane characteristics under changing climate conditions, relationships between climate variables and hurricane parameters are investigated. Climate variables considered for the investigation include regional sea surface temperatures and regional relative humidity. The impact on hurricane characteristics is studied with respect to the central pressure difference and the ratio of the number of major hurricanes to the number of total hurricanes. The future hurricane parameters for near- and long-term projections considering a particular climate scenario (RCP8.5) by the Intergovernmental Panel on Climate Change are predicted using nonlinear autoregressive neural networks. Hurricane tracks are developed for four study regions along the southeastern US coast, and associated economic losses are estimated using a natural hazard analysis tool. The result shows that the estimated losses under changing climate conditions increases dramatically in all study regions. The findings of this study indicate the urgency and necessity of considering climate change scenarios in building design codes and standards.
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      Hurricane Risk Assessment for Residential Buildings in the Southeastern US Coastal Region in Changing Climate Conditions Using Artificial Neural Networks

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4267445
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    contributor authorChi-Ying Lin
    contributor authorEun Jeong Cha
    date accessioned2022-01-30T20:58:45Z
    date available2022-01-30T20:58:45Z
    date issued8/1/2020 12:00:00 AM
    identifier other%28ASCE%29NH.1527-6996.0000390.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4267445
    description abstractHurricanes cause severe damage along US coastal regions, which may be aggravated in changing climate conditions. This paper investigates the impact of climate change on the hurricane risk for residential buildings in the southeastern US coastal region. To understand hurricane characteristics under changing climate conditions, relationships between climate variables and hurricane parameters are investigated. Climate variables considered for the investigation include regional sea surface temperatures and regional relative humidity. The impact on hurricane characteristics is studied with respect to the central pressure difference and the ratio of the number of major hurricanes to the number of total hurricanes. The future hurricane parameters for near- and long-term projections considering a particular climate scenario (RCP8.5) by the Intergovernmental Panel on Climate Change are predicted using nonlinear autoregressive neural networks. Hurricane tracks are developed for four study regions along the southeastern US coast, and associated economic losses are estimated using a natural hazard analysis tool. The result shows that the estimated losses under changing climate conditions increases dramatically in all study regions. The findings of this study indicate the urgency and necessity of considering climate change scenarios in building design codes and standards.
    publisherASCE
    titleHurricane Risk Assessment for Residential Buildings in the Southeastern US Coastal Region in Changing Climate Conditions Using Artificial Neural Networks
    typeJournal Paper
    journal volume21
    journal issue3
    journal titleNatural Hazards Review
    identifier doi10.1061/(ASCE)NH.1527-6996.0000390
    page13
    treeNatural Hazards Review:;2020:;Volume ( 021 ):;issue: 003
    contenttypeFulltext
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