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Natural Hazards Review最新文献

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Modeling Dynamics of Community Resilience to Extreme Events with Explainable Deep Learning 基于可解释深度学习的社区极端事件弹性动力学建模
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-05-01 DOI: 10.1061/nhrefo.nheng-1696
Haiyan Hao, Yan Wang
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引用次数: 2
IPO-Based Evaluation of Sustainable Postdisaster Recovery from Stakeholder Perspectives of the Lushan Earthquake 基于IPO的芦山地震灾后可持续恢复评估
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-05-01 DOI: 10.1061/nhrefo.nheng-1596
Lu Gan, Lihong Wei, Shanhui Huang, Chaoping Hou, Ying Zhou, Bernard T. Han
{"title":"IPO-Based Evaluation of Sustainable Postdisaster Recovery from Stakeholder Perspectives of the Lushan Earthquake","authors":"Lu Gan, Lihong Wei, Shanhui Huang, Chaoping Hou, Ying Zhou, Bernard T. Han","doi":"10.1061/nhrefo.nheng-1596","DOIUrl":"https://doi.org/10.1061/nhrefo.nheng-1596","url":null,"abstract":"","PeriodicalId":51262,"journal":{"name":"Natural Hazards Review","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49670067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Relationship between Coastal Hazard Countermeasures and Community Resilience in the Tōhoku Region of Japan Following the 2011 Tsunami 2011年海啸后Tōhoku日本地区沿海灾害对策与社区恢复力的关系
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-05-01 DOI: 10.1061/nhrefo.nheng-1401
Robert F. Wachter, D. Forcellini, Jessica McManus Warnell, K. Walsh
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引用次数: 3
Closure to “Revisiting Basic Wind Speed of Metro Cities of India” “重温印度地铁城市基本风速”结束
3区 工程技术 Q1 Social Sciences Pub Date : 2023-05-01 DOI: 10.1061/nhrefo.nheng-1709
Suresh Kumar K, Rahul PS, Sihan Li, Mike Gibbons
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引用次数: 0
Drivers of Household Preparedness for Natural Hazards: The Mediating Role of Perceived Coping Efficacy 家庭对自然灾害的准备驱动因素:感知应对效能的中介作用
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-05-01 DOI: 10.1061/nhrefo.nheng-1620
Qing Miao, Fengxiu Zhang
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引用次数: 0
Knowledge-Informed Data-Driven Modeling of Coupled Human-Built–Natural Systems: The Case of Hurricane-Induced Debris 基于知识的数据驱动的人类-建筑-自然耦合系统建模:以飓风引发的碎片为例
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-05-01 DOI: 10.1061/nhrefo.nheng-1705
Catalina González-Dueñas, Mitchell M. Meads, J. Padgett, W. Highfield
{"title":"Knowledge-Informed Data-Driven Modeling of Coupled Human-Built–Natural Systems: The Case of Hurricane-Induced Debris","authors":"Catalina González-Dueñas, Mitchell M. Meads, J. Padgett, W. Highfield","doi":"10.1061/nhrefo.nheng-1705","DOIUrl":"https://doi.org/10.1061/nhrefo.nheng-1705","url":null,"abstract":"","PeriodicalId":51262,"journal":{"name":"Natural Hazards Review","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45011701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Agent-Based Simulation of Spontaneous Volunteer Convergence to Improve Disaster Planning 基于Agent的自发志愿者融合仿真改进灾害规划
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-05-01 DOI: 10.1061/nhrefo.nheng-1659
Kyle Paret, Sebastian A. Rodriguez, M. Mayorga, Lucia Velotti, Emmett J. Lodree
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引用次数: 0
Research in the Field of Natural Hazards Based on Bibliometric Analysis 基于文献计量分析的自然灾害研究
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-05-01 DOI: 10.1061/nhrefo.nheng-1739
Yuandong Huang, Chong Xu, Xujiao Zhang, Lei Li, Xi-wei Xu
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引用次数: 4
Stability and Deformation Degree of Wheel Loaders under the Impact of Debris Flow 泥石流作用下轮式装载机的稳定性及变形程度
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-05-01 DOI: 10.1061/nhrefo.nheng-1552
Zhenmin Feng, Dongmei Huang, Yuan-zhi Zhang, Y. Li, Ruichen Li, Yupeng Sun, Chaoyi Wang
{"title":"Stability and Deformation Degree of Wheel Loaders under the Impact of Debris Flow","authors":"Zhenmin Feng, Dongmei Huang, Yuan-zhi Zhang, Y. Li, Ruichen Li, Yupeng Sun, Chaoyi Wang","doi":"10.1061/nhrefo.nheng-1552","DOIUrl":"https://doi.org/10.1061/nhrefo.nheng-1552","url":null,"abstract":"","PeriodicalId":51262,"journal":{"name":"Natural Hazards Review","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46359320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Perceived Vulnerability to Disease, Resilience, and Mental Health Outcome of Korean Immigrants amid the COVID-19 Pandemic: A Machine Learning Approach 在COVID-19大流行期间,韩国移民对疾病的感知脆弱性、恢复力和心理健康结果:一种机器学习方法
IF 2.7 3区 工程技术 Q1 Social Sciences Pub Date : 2023-05-01 DOI: 10.1061/nhrefo.nheng-1441
Shinwoo Choi, Y. J. Kim, B. Nam, Joo Young Hong, Cristy E. Cummings
This study examined the predictive ability of perceived vulnerability to disease (PVD), fear of COVID-19, and coping mechanisms on the Korean immigrants' psychological distress level amid the pandemic. Through purposive sampling, both foreign-born and US-born Korean immigrants residing in the US above the age of 18 years were invited to an online survey. Between May and June 2020, data collection took place, which yielded the final sample of 790 participants from 42 states. An artificial neural network (ANN) was used to verify variables that predict the level of psychological distress on the participants. The model with one hidden layer holding six hidden neurons showed the best performance. The error rate was approximately 27%, and the results from the sensitivity analysis, the receiver operating characteristics (ROC) curve, showed that the area under the curve (AUC) was 0.801. The most powerful predicting variables in the neural network were resilience, PVD, and social support. Implications for practice and policy are discussed.
本研究考察了感知疾病脆弱性(PVD)、对新冠肺炎的恐惧以及应对机制对韩国移民在疫情期间心理困扰水平的预测能力。通过有目的的抽样,18岁以上居住在美国的外国出生和美国出生的韩国移民都被邀请参加一项在线调查。2020年5月至6月,进行了数据收集,最终产生了来自42个州的790名参与者的样本。使用人工神经网络(ANN)来验证预测参与者心理痛苦程度的变量。具有一个包含六个隐藏神经元的隐藏层的模型显示出最佳性能。错误率约为27%,敏感性分析的结果,即受试者工作特性(ROC)曲线,显示曲线下面积(AUC)为0.801。神经网络中最有力的预测变量是弹性、PVD和社会支持。讨论了对实践和政策的影响。
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引用次数: 0
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Natural Hazards Review
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