{"title":"Rainstorm-Induced Emergency Recognition from Citizens’ Communications Based on Spatial Feature Extraction and Transfer Learning","authors":"Zhao-ge Liu, Xiang-yang Li, Xiao-han Zhu, Chong Wu","doi":"10.1061/(asce)nh.1527-6996.0000591","DOIUrl":"https://doi.org/10.1061/(asce)nh.1527-6996.0000591","url":null,"abstract":"","PeriodicalId":51262,"journal":{"name":"Natural Hazards Review","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48266426","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}
Pub Date : 2023-02-01DOI: 10.1061/(asce)nh.1527-6996.0000604
T. Tomiczek, J. Helgeson, E. Sutley, Donghwan Gu, Sara Hamideh, P. Crawford
{"title":"A Framework for Characterizing Uncertainty Factors in Postdisaster Structural Performance Assessment Data","authors":"T. Tomiczek, J. Helgeson, E. Sutley, Donghwan Gu, Sara Hamideh, P. Crawford","doi":"10.1061/(asce)nh.1527-6996.0000604","DOIUrl":"https://doi.org/10.1061/(asce)nh.1527-6996.0000604","url":null,"abstract":"","PeriodicalId":51262,"journal":{"name":"Natural Hazards Review","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47599955","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}
Pub Date : 2023-02-01DOI: 10.1061/(asce)nh.1527-6996.0000600
Catalina Miranda, J. Becker, Charlotte Toma, Lauren J. Vinnell
{"title":"Homeowners’ Perceptions of Seismic Building Performance and Implications for Preparedness in New Zealand","authors":"Catalina Miranda, J. Becker, Charlotte Toma, Lauren J. Vinnell","doi":"10.1061/(asce)nh.1527-6996.0000600","DOIUrl":"https://doi.org/10.1061/(asce)nh.1527-6996.0000600","url":null,"abstract":"","PeriodicalId":51262,"journal":{"name":"Natural Hazards Review","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44821805","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}
Pub Date : 2023-02-01DOI: 10.1061/nhrefo.nheng-1543
Jeremy R. Porter, Michael L. Marston, Evelyn Shu, Mark Bauer, Kelvin Lai, Bradley Wilson, Mariah Pope
Flooding has been the most costly natural disaster over the last 2 decades within the US. Therefore, recent research has focused on more accurately predicting economic losses from flooding to aid decision makers and mitigate economic exposure. For this, depth–damage functions have commonly been employed to predict the relative or absolute damage to buildings caused by different magnitudes of flooding. Although depth–damage functions, such as those adopted by the US Army Corps of Engineers, are widely available for fluvial and coastal flooding, less work has been done to develop functions for pluvial-induced flooding. Here, we use a database containing 13.5 million claims to develop pluvial depth–damage functions. For this, recently released flood hazard data are utilized to identify claims within the database that are likely related to pluvial flooding. We employed two types of regression models to fit the depth–damage functions. Secondarily, we developed an automated valuation model (AVM) to estimate building values across the state of New Jersey. These building values were then combined with flood hazard layers in order to apply the depth–damage functions and compute an aggregate annualized loss for New Jersey. The results indicated moderate agreement between the observed damage within the state of New Jersey and that computed by applying the study-developed depth–damage curves to buildings within the state using pluvial flood hazard layers. It is anticipated that the depth–damage functions developed by this research will aid future work in more accurately quantifying the economic risks associated with flooding across the US.
{"title":"Estimating Pluvial Depth–Damage Functions for Areas within the United States Using Historical Claims Data","authors":"Jeremy R. Porter, Michael L. Marston, Evelyn Shu, Mark Bauer, Kelvin Lai, Bradley Wilson, Mariah Pope","doi":"10.1061/nhrefo.nheng-1543","DOIUrl":"https://doi.org/10.1061/nhrefo.nheng-1543","url":null,"abstract":"Flooding has been the most costly natural disaster over the last 2 decades within the US. Therefore, recent research has focused on more accurately predicting economic losses from flooding to aid decision makers and mitigate economic exposure. For this, depth–damage functions have commonly been employed to predict the relative or absolute damage to buildings caused by different magnitudes of flooding. Although depth–damage functions, such as those adopted by the US Army Corps of Engineers, are widely available for fluvial and coastal flooding, less work has been done to develop functions for pluvial-induced flooding. Here, we use a database containing 13.5 million claims to develop pluvial depth–damage functions. For this, recently released flood hazard data are utilized to identify claims within the database that are likely related to pluvial flooding. We employed two types of regression models to fit the depth–damage functions. Secondarily, we developed an automated valuation model (AVM) to estimate building values across the state of New Jersey. These building values were then combined with flood hazard layers in order to apply the depth–damage functions and compute an aggregate annualized loss for New Jersey. The results indicated moderate agreement between the observed damage within the state of New Jersey and that computed by applying the study-developed depth–damage curves to buildings within the state using pluvial flood hazard layers. It is anticipated that the depth–damage functions developed by this research will aid future work in more accurately quantifying the economic risks associated with flooding across the US.","PeriodicalId":51262,"journal":{"name":"Natural Hazards Review","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858851","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：