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Natural Hazards Review最新文献

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Spatial Flood Forecasting Modeling under Lack of Data Using RS and Optimized Support Vector Machine: A Case Study of the Zahedan Watershed 基于RS和优化支持向量机的数据缺乏下的空间洪水预报建模——以扎黑丹流域为例
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-08-01 DOI: 10.1061/nhrefo.nheng-1665
Seyyed Meisam Mirkazemi, N. Jalalkamali, Mohsen Irandoost
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引用次数: 1
Effects of Water Content on the Dynamic Response of the Railway Cut Slope Subjected to Heavy-Haul Trains Cyclic Loading 含水率对重载列车循环荷载下铁路路堑边坡动力响应的影响
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-08-01 DOI: 10.1061/nhrefo.nheng-1586
Q. Xie, Zhi-hui Wu, Zhilin Cao, Y. Ban, Xiang Fu, Weichen Sun, Jie Dong
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引用次数: 0
Machine Learning-Based Seismic Damage Assessment of Residential Buildings Considering Multiple Earthquake and Structure Uncertainties 考虑多重地震和结构不确定性的基于机器学习的住宅震害评估
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-08-01 DOI: 10.1061/nhrefo.nheng-1681
Xinzhe Yuan, Liujun Li, Haibin Zhang, Yanping Zhu, Ge-wei Chen, C. Dagli
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引用次数: 1
Disaster Education in the Context of Postsecondary Education: A Systematic Literature Review 中学后教育背景下的灾害教育:系统文献综述
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-08-01 DOI: 10.1061/nhrefo.nheng-1778
Mahed-Ul-Islam Choudhury, Haorui Wu
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引用次数: 0
Effects of Climate Change and Urbanization on Bridge Flood Vulnerability: A Regional Assessment for Harris County, Texas 气候变化和城市化对桥梁洪水脆弱性的影响——以德克萨斯州哈里斯县为例
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-08-01 DOI: 10.1061/nhrefo.nheng-1720
Fahad Pervaiz, M. Hummel
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引用次数: 1
Capacity-Building to Support Safer Housing through Appropriate Hurricane Strap Use 通过适当使用防飓风带支持更安全住房的能力建设
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-08-01 DOI: 10.1061/nhrefo.nheng-1708
Briar Goldwyn, Cole Velasquez, A. Liel, Amy Javernick‐Will, Matthew A. Koschmann
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引用次数: 0
Navigating Theories of Actions on Disaster Prevention: A Systematic Review on Disaster Research in Japan 防灾行动理论的导航:日本灾害研究的系统回顾
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-05-01 DOI: 10.1061/nhrefo.nheng-1630
H. Daimon, Ryohei Miyamae, Wen-Jiun Wang
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引用次数: 2
Ember Alerts: Assessing Wireless Emergency Alert Messages in Wildfires Using the Warning Response Model 余烬警报:使用警报响应模型评估野火中的无线紧急警报信息
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-05-01 DOI: 10.1061/nhrefo.nheng-1724
E. Kuligowski, Nicholas Waugh, J. Sutton, T. Cova
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引用次数: 4
Expanding Vulnerability Indices for Pandemic Effects 扩大流行病影响脆弱性指数
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-05-01 DOI: 10.1061/nhrefo.nheng-1575
Kaveh Faraji Najarkolaie, M. Bensi, Ryan Dadmun, Courtney E Romolt, Yalda Saadat, Nicolette Louissaint
In this study, our goal is to identify potentially vulnerable communities that could be subject to ongoing or compounding impacts from the pandemic and/or that may experience a slower recovery due to sociodemographic factors. For this purpose, we compiled information from multiple databases related to sociodemographic and health variables. We used a ranking-based method to integrate them and develop new combined indices. We also investigated a time-dependent correlation between vulnerability components and COVID-19 statistics to understand their time-dependent relationship. We ultimately developed pandemic vulnerability indices by combining CDC's social vulnerability index, our newly developed composite health vulnerability index, and COVID-19 impact indices. We also considered additional assessments include expected annual loss due to natural hazards and community resilience. Potential hot spots (at the county level) were identified throughout the United States, and some general trends were noted. Counties with high COVID-19 impact indices and higher values of the pandemic vulnerability indices were primarily located in the southern United States or coastal areas in the Eastern and Southwestern United States at the beginning of the COVID-19 pandemic. Over time, the computed pandemic vulnerability indices shifted to higher values for counties in the southern and north-central United States, while values calculated for the northwestern and northeastern communities tended to decrease.
在这项研究中,我们的目标是确定可能受到流行病持续或复合影响和/或由于社会人口因素可能经历较慢恢复的潜在脆弱社区。为此,我们从与社会人口和健康变量相关的多个数据库中收集了信息。我们采用基于排名的方法对它们进行整合,形成新的组合指标。我们还研究了漏洞成分与COVID-19统计数据之间的时间依赖相关性,以了解它们的时间依赖关系。最终,我们将CDC的社会脆弱性指数、我们新开发的综合健康脆弱性指数和COVID-19影响指数相结合,制定了大流行脆弱性指数。我们还考虑了额外的评估,包括自然灾害和社区恢复能力造成的预期年度损失。在美国各地确定了潜在的热点(在县一级),并注意到一些一般趋势。COVID-19影响指数和大流行脆弱性指数较高的县主要位于美国南部或美国东部和西南部沿海地区。随着时间的推移,计算出的大流行脆弱性指数在美国南部和中北部的县转向较高的值,而在西北部和东北部社区的计算值则趋于下降。
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引用次数: 0
Barriers to Postdisaster Housing Reconstruction: Issues of Place and Power Mismatch 灾后住房重建的障碍:地点与权力不匹配的问题
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-05-01 DOI: 10.1061/nhrefo.nheng-1530
J. Talbot, C. Poleacovschi, Jane M. Rongerude
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引用次数: 0
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Natural Hazards Review
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