Data-Driven Telecommunication Outage Prediction during Hurricane Events
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10, Issue 3
Abstract
Telecommunication infrastructure (TI) has become an indispensable part in modern society, and its functionality is especially vital to emergency response during hurricanes. This study bridges the gap of lack of quantitative TI outage prediction models during hurricane events. County-level TI outage and power outage time-series and demographic data across eight continental US states during 10 recent hurricane events are collected. Two types of TI outage prediction models, namely, time-independent and time-dependent models, are developed. The time-independent model is intended for rapid prehurricane preparation or posthurricane outage evaluation, and is based on the partial least-squares regression technique. Relative predictor importance is also quantified via the Shapley additive explanations for better model interpretability. Moreover, to offer temporal TI outage prediction as the hurricane unfolds in real time, the time-dependent TI outage prediction model was developed, which leverages recent advances in recurrent neural networks such as the long short-term memory (LSTM) and bidirectional LSTM networks. The time-dependent model is able to handle time-series data and offers sequential TI outage prediction as new observations become available. Comprehensive model predictive performance evaluation is carried out and the explanatory power of different predictor combinations are examined. The proposed data-driven models can offer the much-needed quantitative and rapid TI outage prediction during hurricane events.
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Data Availability Statement
Computer codes that support the findings of this study are available from the corresponding author upon reasonable request. The power outage data used during the study were provided by a third party, PowerOutage.US. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
The author gratefully acknowledges the valuable review comments by the two anonymous reviewers in improving the quality of the manuscript. We also appreciate the power outage data provided by PowerOutage.US.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10 • Issue 3 • September 2024
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© 2024 American Society of Civil Engineers.
History
Received: Nov 25, 2023
Accepted: Feb 12, 2024
Published online: Jun 12, 2024
Published in print: Sep 1, 2024
Discussion open until: Nov 12, 2024
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