A. James Clark School of Engineering
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Item DATA-DRIVEN ASSESSMENT FOR UNDERSTANDING THE IMPACTS OF LOCALIZED HAZARDS(2022) Ghaedi, Hamed; Reilly, Allison C.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Both 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.Item Predicting flood damage using the Flood Peak Ratio and Giovanni Flooded Fraction - Code(2022-07-11) Ghaedi, Hamed; Baroud, Hiba; Ferreira, Celso; Perrucci, Daniel; Reilly, Allison; Reilly, AllisonA spatially-resolved understanding of the intensity of a flood hazard is required for accurate predictions of infrastructure reliability and losses in the aftermath. Currently, researchers who wish to predict flood losses or infrastructure reliability following a flood usually rely on computationally intensive hydrodynamic modeling or on flood hazard maps (e.g., the 100-year floodplain) to build a spatially-resolved understanding of the flood’s intensity. However, both have specific limitations. The former requires both subject matter expertise to create the models and significant computation time, while the latter is a static metric that provides no variation among specific events. The objective of this work is to develop an integrated data-driven approach to rapidly predict flood damages using two emerging flood intensity heuristics, namely the Flood Peak Ratio (FPR) and NASA’s Giovanni Flooded Fraction (GFF). This study uses data on flood claims from the National Flood Insurance Program (NFIP) to proxy flood damage, along with other well-established flood exposure variables, such as regional slope and population. The approach uses statistical learning methods to generate predictive models at two spatial levels: nationwide and statewide for the entire contiguous United States. A variable importance analysis demonstrates the significance of FPR and GFF data in predicting flood damage. In addition, the model performance at the state-level was higher than the nationwide level analysis, indicating the effectiveness of both FPR and GFF models at the regional level. A data-driven approach to predict flood damage using the FPR and GFF data offer promise considering their relative simplicity, their reliance on publicly accessible data, and their comparatively fast computational speed.Item Predicting flood damage using the Flood Peak Ratio and Giovanni Flooded Fraction - Dataset(2022-07-11) Ghaedi, Hamed; Baroud, Hiba; Perrucci, Daniel; Ferreira, Celso; Reilly, Allison; Reilly, AllisonA spatially-resolved understanding of the intensity of a flood hazard is required for accurate predictions of infrastructure reliability and losses in the aftermath. Currently, researchers who wish to predict flood losses or infrastructure reliability following a flood usually rely on computationally intensive hydrodynamic modeling or on flood hazard maps (e.g., the 100-year floodplain) to build a spatially-resolved understanding of the flood’s intensity. However, both have specific limitations. The former requires both subject matter expertise to create the models and significant computation time, while the latter is a static metric that provides no variation among specific events. The objective of this work is to develop an integrated data-driven approach to rapidly predict flood damages using two emerging flood intensity heuristics, namely the Flood Peak Ratio (FPR) and NASA’s Giovanni Flooded Fraction (GFF). This study uses data on flood claims from the National Flood Insurance Program (NFIP) to proxy flood damage, along with other well-established flood exposure variables, such as regional slope and population. The approach uses statistical learning methods to generate predictive models at two spatial levels: nationwide and statewide for the entire contiguous United States. A variable importance analysis demonstrates the significance of FPR and GFF data in predicting flood damage. In addition, the model performance at the state-level was higher than the nationwide level analysis, indicating the effectiveness of both FPR and GFF models at the regional level. A data-driven approach to predict flood damage using the FPR and GFF data offer promise considering their relative simplicity, their reliance on publicly accessible data, and their comparatively fast computational speed.