Measuring and Enhancing the Resilience of Interdependent Power Systems, Emergency Services, and Social Communities

Abstract

Several calamities occur throughout the world each year, resulting in varying losses. Disasters wreak havoc on infrastructures and impair operation. They result in human deaths and injuries and stress people's mental and emotional states. These negative impacts of natural disasters induce significant economic losses, as demonstrated by the $ 423 billion loss in 2011 in Tohoku, Japan, and the $ 133 billion loss in hurricane Harvey, U.S.A. Every year, hurricanes and tropical storms result in 10,000 human deaths worldwide. To mitigate losses, communities' readiness, flexibility, and resilience must be strengthened. To this end, appropriate techniques for forecasting a community's capacity and functionality in the face of impending crises must be developed and suitable community resilience metrics and their quantification must be established. Collaboration between critical infrastructures such as power systems and emergency services and social networks is critical for building a resilient community. As a result, we require metrics that account for both the social and infrastructure aspects of the community. While the literature on critical infrastructures such as power systems discusses the effect of social factors on resilience, they do not model these social factors and metrics due to their complexity. On the other hand, it turns out that the role of critical infrastructures and some critical social characteristics is overlooked in the computational social science literature on community resilience. Thus, this dissertation presents a multi-agent socio-technical model of community resilience, taking into account the interconnection of power systems, emergency services, and social communities. We offer relevant measures for each section and describe dynamic change and its dependence on other metrics using a variety of theories and expertise from social science, psychology, electrical engineering, and emergency services. To validate the model, we used data on two hurricanes (Irma and Harvey) collected from Twitter, GoogleTrends, FEMA, power utilities, CNN, and Snopes (a fact-checking organization). We also describe methods for quantifying social metrics such as anxiety, risk perception, cooperation using social sensing, natural language processing, and text mining tools.