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Gina Tonn

Bio: Gina Tonn is an academic researcher from Delaware Department of Natural Resources and Environmental Control. The author has contributed to research in topics: Flood myth & Flooding (psychology). The author has an hindex of 7, co-authored 10 publications receiving 106 citations. Previous affiliations of Gina Tonn include Johns Hopkins University & University of Pennsylvania.

Papers
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Journal ArticleDOI
TL;DR: This study lends insight into priorities for future work, including the development of more in-depth behavioral and decision rules at the individual and community level, as well as a new modeling approach for integrating behavior, policy, flood hazards, and engineering interventions.
Abstract: Although individual behavior plays a major role in community flood risk, traditional flood risk models generally do not capture information on how community policies and individual decisions impact the evolution of flood risk over time. The purpose of this study is to improve the understanding of the temporal aspects of flood risk through a combined analysis of the behavioral, engineering, and physical hazard aspects of flood risk. Additionally, the study aims to develop a new modeling approach for integrating behavior, policy, flood hazards, and engineering interventions. An agent-based model (ABM) is used to analyze the influence of flood protection measures, individual behavior, and the occurrence of floods and near-miss flood events on community flood risk. The ABM focuses on the following decisions and behaviors: dissemination of flood management information, installation of community flood protection, elevation of household mechanical equipment, and elevation of homes. The approach is place based, with a case study area in Fargo, North Dakota, but is focused on generalizable insights. Generally, community mitigation results in reduced future damage, and individual action, including mitigation and movement into and out of high-risk areas, can have a significant influence on community flood risk. The results of this study provide useful insights into the interplay between individual and community actions and how it affects the evolution of flood risk. This study lends insight into priorities for future work, including the development of more in-depth behavioral and decision rules at the individual and community level.

52 citations

Journal ArticleDOI
TL;DR: Results indicate that the annual number of transport-related companies affected by cyber incidents and the associated costs are on the rise, and insurance purchase can be an important risk management strategy to allow transportation infrastructure systems to recover from cyber incidents.

25 citations

Journal ArticleDOI
TL;DR: In this paper, the authors identify barriers to and opportunities for lowering transportation-related disaster losses and for improving infrastructure risk management including the need for better data and metrics to support resilience.

19 citations

Journal ArticleDOI
11 May 2017
TL;DR: A validated power-outage forecasting model is used in conjunction with an agent-based model to characterize how a community’s likelihood of losing power in repeated hurricanes is affected by the complex interactions among individuals’ behavioral responses in whether to engage in personal or collective action.
Abstract: Hurricanes produce significant, widespread, and often prolonged electrical-power outages For example, Hurricane Irene caused more than 500 000 Long Island Power Authority customers to lose power and it took eight days to achieve 99% customer restoration Individuals and businesses are heavily dependent on a continuous supply of electricity Given this strong dependence on reliable electricity, individuals and private industries are increasingly putting collective pressure on regulators to require system hardening by utilities In some cases, this has led to utility action Conversely, many customers install a backup generator to guarantee electricity supply during disruptive events These actions taken by individual customers affect their experiences in future storms, and are generally influenced by individuals’ strength of preference for reliable power, their beliefs about the likelihood of losing power in the future, and the outcomes of their most recent experiences However, individual action may come at the expense of collective action, whereby those who buy generators, often those with more resources available to purchase the generator, do not participate in the collective grievance, reducing the demand for overall system hardening By using a validated power-outage forecasting model in conjunction with an agent-based model, we characterize how a community’s likelihood of losing power in repeated hurricanes is affected by the complex interactions among individuals’ behavioral responses in whether to engage in personal or collective action

18 citations

Journal ArticleDOI
TL;DR: This analysis provided insight on how rainfall and storm surge, along with wind, contribute to power outages in hurricanes and indicates that inclusion of other covariates, particularly precipitation, may improve model accuracy and robustness across a range of storm conditions and geography.
Abstract: In August 2012, Hurricane Isaac, a Category 1 hurricane at landfall, caused extensive power outages in Louisiana. The storm brought high winds, storm surge, and flooding to Louisiana, and power outages were widespread and prolonged. Hourly power outage data for the state of Louisiana were collected during the storm and analyzed. This analysis included correlation of hourly power outage figures by zip code with storm conditions including wind, rainfall, and storm surge using a nonparametric ensemble data mining approach. RESULTS were analyzed to understand how correlation of power outages with storm conditions differed geographically within the state. This analysis provided insight on how rainfall and storm surge, along with wind, contribute to power outages in hurricanes. By conducting a longitudinal study of outages at the zip code level, we were able to gain insight into the causal drivers of power outages during hurricanes. Our analysis showed that the statistical importance of storm characteristic covariates to power outages varies geographically. For Hurricane Isaac, wind speed, precipitation, and previous outages generally had high importance, whereas storm surge had lower importance, even in zip codes that experienced significant surge. The results of this analysis can inform the development of power outage forecasting models, which often focus strictly on wind-related covariates. Our study of Hurricane Isaac indicates that inclusion of other covariates, particularly precipitation, may improve model accuracy and robustness across a range of storm conditions and geography. Language: en

17 citations


Cited by
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Book ChapterDOI
31 Jan 2012
TL;DR: This article presents an agent-based computational model of civil violence, which shows that a central authority seeks to suppress decentralized rebellion and communal violence between two warring ethnic groups.
Abstract: This article presents an agent-based computational model of civil violence. Two variants of the civil violence model are presented. In the first a central authority seeks to suppress decentralized rebellion. In the second a central authority seeks to suppress communal violence between two warring ethnic groups.

277 citations

Journal ArticleDOI
TL;DR: In this paper, an integrated resilience response framework is proposed, which not only links the situational awareness with resilience enhancement, but also provides effective and efficient responses in both preventive and emergency states.
Abstract: Boosting the resilience of power systems is one of the core requirements of smart grid. In this paper, an integrated resilience response framework is proposed, which not only links the situational awareness with resilience enhancement, but also provides effective and efficient responses in both preventive and emergency states. The core of the proposed framework is a two-stage robust mixed-integer optimization model, whose mathematical formulation is presented in this paper as well. To solve the above model, an algorithm based on the nested column-and-constraint generation decomposition is provided, and computational efficiency improvement techniques are proposed. Preventive response in this paper considers generator re-dispatch and topology switching, while emergency response includes generator re-dispatch, topology switching and load shedding. Several numerical simulations validate the effectiveness of the proposed framework and the efficiency of the solution methodology. Key findings include the following: 1) in terms of enhancing power grid resilience, the integrated resilience response is preferable to both independent preventive response and independent emergency response; 2) the power grid resilience could be further enhanced by utilizing topology switching in the integrated resilience response.

210 citations

Journal ArticleDOI
TL;DR: The development of a hurricane power outage prediction model applicable along the full U.S. coastline is described, the use of the model is demonstrated for Hurricane Sandy, and what the impacts of a number of historic storms, including Typhoon Haiyan, would be on current U.s. energy infrastructure are estimated.
Abstract: Hurricanes regularly cause widespread and prolonged power outages along the U.S. coastline. These power outages have significant impacts on other infrastructure dependent on electric power and on the population living in the impacted area. Efficient and effective emergency response planning within power utilities, other utilities dependent on electric power, private companies, and local, state, and federal government agencies benefit from accurate estimates of the extent and spatial distribution of power outages in advance of an approaching hurricane. A number of models have been developed for predicting power outages in advance of a hurricane, but these have been specific to a given utility service area, limiting their use to support wider emergency response planning. In this paper, we describe the development of a hurricane power outage prediction model applicable along the full U.S. coastline using only publicly available data, we demonstrate the use of the model for Hurricane Sandy, and we use the model to estimate what the impacts of a number of historic storms, including Typhoon Haiyan, would be on current U.S. energy infrastructure.

134 citations