Graph-based Cascading Impact Estimation for Identifying Crucial Infrastructure Components

Sangkeun Lee; Supriya Chintavali; Sarah Tennille; Junghoon Chae; Anika Tabassum; Varisara Tansakul; Daniel Redmon; Robert Moncrief; Aaron Myers

doi:10.1109/BigData55660.2022.10020811

Graph-based Cascading Impact Estimation for Identifying Crucial Infrastructure Components

Sangkeun Lee, Supriya Chintavali, Sarah Tennille, Junghoon Chae, Anika Tabassum, Varisara Tansakul, Daniel Redmon, Robert Moncrief, Aaron Myers

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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™) system, which is a situational-awareness system operated by ORNL for the department of energy of the United States.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
Editors	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6749-6751
Number of pages	3
ISBN (Electronic)	9781665480451
DOIs	https://doi.org/10.1109/BigData55660.2022.10020811
State	Published - 2022
Event	2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan Duration: Dec 17 2022 → Dec 20 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference	2022 IEEE International Conference on Big Data, Big Data 2022
Country/Territory	Japan
City	Osaka
Period	12/17/22 → 12/20/22

Funding

This manuscript has been authored by UT-Battelle LLC under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes.DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

electric grid
extreme weather
machine learning
outages
winter storms

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BigData55660.2022.10020811

Cite this

Lee, S., Chintavali, S., Tennille, S., Chae, J., Tabassum, A., Tansakul, V., Redmon, D., Moncrief, R., & Myers, A. (2022). Graph-based Cascading Impact Estimation for Identifying Crucial Infrastructure Components. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (pp. 6749-6751). (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData55660.2022.10020811

Lee, Sangkeun ; Chintavali, Supriya ; Tennille, Sarah et al. / Graph-based Cascading Impact Estimation for Identifying Crucial Infrastructure Components. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 6749-6751 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

@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",

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Lee, S , Chintavali, S , Tennille, S , Chae, J , Tabassum, A, Tansakul, V, Redmon, D, Moncrief, R & Myers, A 2022, Graph-based Cascading Impact Estimation for Identifying Crucial Infrastructure Components. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, Institute of Electrical and Electronics Engineers Inc., pp. 6749-6751, 2022 IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, 12/17/22. https://doi.org/10.1109/BigData55660.2022.10020811

Graph-based Cascading Impact Estimation for Identifying Crucial Infrastructure Components. / Lee, Sangkeun ; Chintavali, Supriya ; Tennille, Sarah et al.
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. p. 6749-6751 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Graph-based Cascading Impact Estimation for Identifying Crucial Infrastructure Components

AU - Lee, Sangkeun

AU - Chintavali, Supriya

AU - Tennille, Sarah

AU - Chae, Junghoon

AU - Tabassum, Anika

AU - Tansakul, Varisara

AU - Redmon, Daniel

AU - Moncrief, Robert

AU - Myers, Aaron

PY - 2022

Y1 - 2022

N2 - 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™) system, which is a situational-awareness system operated by ORNL for the department of energy of the United States.

AB - 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™) system, which is a situational-awareness system operated by ORNL for the department of energy of the United States.

KW - electric grid

KW - extreme weather

KW - machine learning

KW - outages

KW - winter storms

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DO - 10.1109/BigData55660.2022.10020811

M3 - Conference contribution

AN - SCOPUS:85147930140

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SP - 6749

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BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

A2 - Tsumoto, Shusaku

A2 - Ohsawa, Yukio

A2 - Chen, Lei

A2 - Van den Poel, Dirk

A2 - Hu, Xiaohua

A2 - Motomura, Yoichi

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A2 - Wu, Lingfei

A2 - Xie, Ying

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PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Lee S , Chintavali S , Tennille S , Chae J , Tabassum A, Tansakul V et al. Graph-based Cascading Impact Estimation for Identifying Crucial Infrastructure Components. In Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, editors, Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 6749-6751. (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). doi: 10.1109/BigData55660.2022.10020811

Graph-based Cascading Impact Estimation for Identifying Crucial Infrastructure Components

Abstract

Publication series

Conference

Funding

Keywords

UN SDGs

Access to Document

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