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393 results on '"Padgett, Jamie E."'

3. Impact of Coastal Hazards on Residents Spatial Accessibility to Health Services

Author

Balomenos, Georgios P., Hu, Yujie, Padgett, Jamie E., and Shelton, Kyle

Subjects
Computer Science - Computers and Society, Statistics - Applications
Abstract

The mobility of residents and their access to essential services can be highly affected by transportation network closures that occur during and after coastal hazard events. Few studies have used geographic information systems coupled with infrastructure vulnerability models to explore how spatial accessibility to goods and services shifts after a hurricane. Models that explore spatial accessibility to health services are particularly lacking. This study provides a framework to examine how the disruption of transportation networks during and after a hurricane can impact a residents ability to access health services over time. Two different bridge closure conditions, inundation and structural failure, along with roadway inundation are used to quantify post-hurricane accessibility at short- and long-term temporal scales. Inundation may close a bridge for hours or days, but a structural failure may close a route for weeks or months. Both forms of closure are incorporated using probabilistic vulnerability models coupled with GIS-based models to assess spatial accessibility in the aftermath of a coastal hazard. Harris County, an area in Southeastern Texas prone to coastal hazards, is used as a case study. The results indicate changes in the accessibility scores of specific areas depending on the temporal scale of interest and intensity of the hazard scenario. Sociodemographic indicators are also examined for the study region, revealing the populations most likely to suffer from lack of accessibility. Overall, the presented framework helps to understand how both short-term functionality loss and long-term damage affect access to critical services such as health care after a hazard. This information, in turn, can shape decisions about future mitigation and planning efforts, while the presented framework can be expanded to other hazard-prone areas.

Published
2020
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14. DesignSafe: New Cyberinfrastructure for Natural Hazards Engineering

Author

Rathje, Ellen M, Dawson, Clint, Padgett, Jamie E, Pinelli, Jean-Paul, Stanzione, Dan, Adair, Ashley, Arduino, Pedro, Brandenberg, Scott J, Cockerill, Tim, Dey, Charlie, Esteva, Maria, Haan, Fred L, Hanlon, Matthew, Kareem, Ahsan, Lowes, Laura, Mock, Stephen, and Mosqueda, Gilberto

Subjects
Industry, Innovation and Infrastructure, Sustainable Cities and Communities, Cyberinfrastructure, High performance computing, Cloud services, Data sharing, Engineering, Psychology and Cognitive Sciences, Strategic, Defence & Security Studies
Published
2017

19. Strengthening and Retrofitting of Steel Bridges

Author

Jara, José M., Jara, Manuel, Olmos, Bertha A., Padgett, Jamie E., de Freitas, Vasco Peixoto, Series editor, Costa, Anibal, Series editor, Delgado, João M.P.Q., Series editor, Costa, Aníbal, editor, Arêde, António, editor, and Varum, Humberto, editor

Published
2018
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35. A Data-Driven Approach to Hurricane Debris Modeling.

Author

González-Dueñas, Catalina, Bernier, Carl, and Padgett, Jamie E.

Subjects
HURRICANES, SEVERE storms, MACHINE learning, STANDARD deviations, STORMS, REMOTE-sensing images
Abstract

The large amount of debris generated in the aftermath of hurricane and storm events can cause severe financial and logistical burdens to coastal communities. Existing debris estimation models mainly focus on wind-induced debris and produce estimates with errors of nearly 50%, highlighting the importance of developing more comprehensive models that can account for other types of debris while improving accuracy. Therefore, the objective of this study is to develop a probabilistic framework to estimate the presence and amount of waterborne debris following a severe storm using machine learning (ML) techniques as a function of relevant storm and landcover parameters. Machine learning techniques are leveraged to generate debris presence and volume models, employing pre- and post-event aerial and satellite imagery and a debris removal database for Hurricane Ike, respectively. The results show that the ensemble learning algorithms perform the best for both tasks, with a misclassification error of 5.56% for the debris presence predictive model, and a normalized root mean squared error (RMSE) value of 11.98 for the debris volume model, the lowest RMSE of the tested algorithms. Dual-layer ML models are also investigated, incorporating the debris presence as a predictor in the debris volume model. The results show a percent error of 11.29% for the dual-layer model and an approximately 5.4% increase in performance with respect to the model that does not incorporate debris presence. The generated debris volume and presence models will provide useful tools to inform decision-making, evaluate mitigation strategies, facilitate recovery efforts, and improve resource allocation following a storm event. [ABSTRACT FROM AUTHOR]

Published
2023
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