@inproceedings{ee7d3ad889f942099692a1f894488e4f,
title = "Graph-based Cascading Impact Estimation for Identifying Crucial Infrastructure Components",
abstract = "Critical Infrastructures (CIs) such as energy, communication, and transportation compose a complex network that sustains day-to-day commodity flows vital to national security, economic stability, and public safety. Failures caused by an extreme weather event or a man-made incident can trigger widespread cascading failures, sending ripple effects at regional or even national scales. To minimize such impact, emergency responders must identify crucial components within CIs during such stressor events in a systematic and quantifiable manner and take appropriate mitigating actions. Oak Ridge National Laboratory (ORNL) has developed a graph-based analytic system named URBAN-NET, which estimates cascading impact caused by the disruption of critical infrastructure components by leveraging the topology of a critical infrastructure network. Before and during critical events (e.g., hurricanes), the URBAN-NET system generates reports that contain the ranking of the most crucial energy components that have the most downstream impact across infrastructure layers. The developed system has been integrated with the Environment for Analysis of Geo-Located Energy Information (EAGLE-I{\texttrademark}) system, which is a situational-awareness system operated by ORNL for the department of energy of the United States.",
keywords = "electric grid, extreme weather, machine learning, outages, winter storms",
author = "Sangkeun Lee and Supriya Chintavali and Sarah Tennille and Junghoon Chae and Anika Tabassum and Varisara Tansakul and Daniel Redmon and Robert Moncrief and Aaron Myers",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",
year = "2022",
doi = "10.1109/BigData55660.2022.10020811",
language = "English",
series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6749--6751",
editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",
booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",
}