Evacuation Zone Modeling under Climate Change: A Data-Driven Method
Publication: Journal of Infrastructure Systems
Volume 23, Issue 4
Abstract
Predetermined evacuation zones can be used to estimate the demand of evacuees, which is helpful in assessing the resilience of transportation systems in the presence of natural disasters. Evacuation zones defined based on current road networks and environmental and demo-economic characteristics of a region cannot remain the same in the future because long-term climate change such as the rise of sea level would have major impacts on hurricane-related risks. Traditional methods for the prediction of future evacuation zones rely heavily on the storm surge models and could be time-consuming and costly to use. This study develops a novel grid cell–based data-driven method that can predict future evacuation zones under climate change without running the expensive storm surge models. The map of Manhattan, which is the central area of New York City, was uniformly split into grid cells as the basic geographical units of analysis. A decision tree and a random forest were used to capture the relationship between grid cell–specific features, such as geographical features, evacuation mobility, and demo-economic features, and current zone categories that could reflect the risk levels during hurricanes. Tenfold cross validation was used to evaluate model performance and it was found that the random forest outperformed the decision tree in term of the accuracy and kappa statistic. The random forest was used to predict the delineation of evacuation zones in the 2050s and 2090s based on the predicted sea-level rises and changes of demo-economic features. Compared with the current zoning, the areas with a need for evacuation are expected to expand in the future. The proposed method can be used to promptly estimate the future evacuation zones under different sea-level rise scenarios and can provide the convenience to assess transportation system resilience in the context of climate change.
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Acknowledgments
This paper is based on work supported by the National Science Foundation under Grant 1541164 Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP): Type 1: Reductionist and Integrative Approaches to Improve the Resilience of Multi-Scale Interdependent Critical Infrastructure. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authors would like to thank Professor Minghua Zhang and his team from the State University of New York at Stony Brook for providing the climate prediction data as a part of the New York State Resiliency Institute for Storms & Emergencies (NYRISE) project. The authors also thank the anonymous reviewers for their valuable comments and suggestions that help improve the paper.
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Published In
Journal of Infrastructure Systems
Volume 23 • Issue 4 • December 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Jun 2, 2016
Accepted: Jan 13, 2017
Published online: Apr 11, 2017
Discussion open until: Sep 11, 2017
Published in print: Dec 1, 2017
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