Statistical and machine learning models for critical infrastructure resilience

Heglund, Jacob Scott White

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https://hdl.handle.net/2142/108533 Copy

Description

Title

Statistical and machine learning models for critical infrastructure resilience

Author(s)

Heglund, Jacob Scott White

Issue Date

2020-07-22

Director of Research (if dissertation) or Advisor (if thesis)

Tran, Huy T

Department of Study

Aerospace Engineering

Discipline

Aerospace Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Critical Infrastructure Resilience
• Machine Learning
• Statistical Modeling
• Graph Neural Networks

Abstract

This thesis presents a data-driven approach to improving predictions of critical infrastructure behaviors. In our first approach, we explore novel data sources and time series modeling techniques to model disaster impacts on power systems through the case study of Hurricane Sandy as it impacted the state of New York. We find a correlation between Twitter data and load forecast errors, suggesting that Twitter data may provide value towards predicting impacts of disasters on infrastructure systems. Based on these findings, we then develop time series forecasting methods to predict the NYISO power system behaviors at the zonal level, utilizing Twitter and load forecast data as model inputs. In our second approach, we develop a novel, graph-based formulation of the British rail network to model the nonlinear cascading delays on the rail network. Using this formulation, we then develop machine learning approaches to predict delays in the rail network. Through experiments on real-world rail data, we find that the selected architecture provides more accurate predictions than other models due to its ability to capture both spatial and temporal dimensions of the data.

Graduation Semester

2020-08

Type of Resource

Thesis

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http://hdl.handle.net/2142/108533

Copyright and License Information

Copyright 2020 Jacob Heglund

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