DATA-DRIVEN ASSESSMENT FOR UNDERSTANDING THE IMPACTS OF LOCALIZED HAZARDS

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dc.contributor.advisorReilly, Allison C.en_US
dc.contributor.authorGhaedi, Hameden_US
dc.contributor.departmentCivil Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2022-09-16T05:36:57Z
dc.date.available2022-09-16T05:36:57Z
dc.date.issued2022en_US
dc.description.abstractBoth the number of disasters in the U.S. and federal outlays following disasters are rising. Thus, evaluating the impact of varying natural hazards on the built environment and communities rapidly and at various spatial scales is of the utmost importance. Many hazards can cause significant and repetitive economic and social damages. This dissertation is a collection of studies that broadly evaluates resilience outcomes in urban areas using data-driven approaches. I do this over three chapters, each of which explores a unique aspect of hazards and their impact on society. The first two chapters are devoted to federal disaster programs aimed at supporting recovery and building resilience. I especially seek to understand how characteristics of hazards intersect with aspects of the physical and social environment to drive federal intervention. The final chapter explores the heterogenous impacts of natural hazards in urban communities and how disparities correlate with various socioeconomic and demographic characteristics. The first two studies examine two major federal disaster programs in the U.S. – FEMA Public Assistance (PA) and FEMA National Flood Insurance Program (NFIP) – at varying spatial and temporal scales. Both leverage parametric and non-parametric statistical learning algorithms to understand how measures of hazard intensity and local factors drive federal intervention. These studies could be used by federal/state-level resource managers for planning the level of aid that may be required after a disaster. This study can also potentially be useful for decision-makers to identify the potential causes of increased disaster spending over time. In the final chapter, I evaluate the links between public transit disruptions, socioeconomic characteristics, and precipitation. By analyzing the spatial distribution and clustering of infrastructure disruptions, I identify the area(s) susceptible to a disproportionate amount of disruptions. Additionally, spatial statistical models are developed to investigate the relationship between infrastructure disruptions and the characteristics of the communities by including variables related to socioeconomic, demographics, social vulnerability, traffic volume, transit system, road connectivity, and the built environment characteristics. For the decision-makers with the goal of improving the performance and resilience of the transit system, this study can provide insight to locate critical areas impacted by such disruptions.en_US
dc.identifierhttps://doi.org/10.13016/crl9-jlgv
dc.identifier.urihttp://hdl.handle.net/1903/29166
dc.language.isoenen_US
dc.subject.pqcontrolledCivil engineeringen_US
dc.subject.pquncontrolledFEMAen_US
dc.subject.pquncontrolledfloodingen_US
dc.subject.pquncontrolledinfrastructureen_US
dc.subject.pquncontrolledlossen_US
dc.subject.pquncontrolledmachine learningen_US
dc.subject.pquncontrollednatural hazarden_US
dc.titleDATA-DRIVEN ASSESSMENT FOR UNDERSTANDING THE IMPACTS OF LOCALIZED HAZARDSen_US
dc.typeDissertationen_US

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