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Record |
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Author |
Laura Szczyrba; Yang Zhang; Duygu Pamukcu; Derya Ipek Eroglu |
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Title |
A Machine Learning Method to Quantify the Role of Vulnerability in Hurricane Damage |
Type |
Conference Article |
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Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
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Issue |
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Pages |
179-187 |
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Keywords |
Vulnerability, Impact, Damage, Machine Learning, Hurricane María. |
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Abstract |
Accurate pre-disaster damage predictions and post-disaster damage assessments are challenging because of the complicated interrelationships between multiple damage drivers, including various natural hazards, as well as antecedent infrastructure quality and demographic characteristics. Ensemble decision trees, a family of machine learning algorithms, are well suited to quantify the role of social vulnerability in disaster impacts because they provide interpretable measures of variable importance for predictions. Our research explores the utility of an ensemble decision tree algorithm, Random Forest Regression, for quantifying the role of vulnerability with a case study of Hurricane Mar\'ia. The contributing predictive power of eight drivers of structural damage was calculated as the decrease in model mean squared error. A measure of social vulnerability was found to be the model's leading predictor of damage patterns. An additional algorithm, other methods of quantifying variable importance, and future work are discussed. |
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Address |
Virginia Tech; Virginia Tech; Virginia Tech; Virginia Tech |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Title |
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Abbreviated Series Title |
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Edition |
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ISSN |
978-1-949373-27-17 |
ISBN |
2411-3403 |
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Track |
Analytical Modeling and Simulation |
Expedition |
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Conference |
17th International Conference on Information Systems for Crisis Response and Management |
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Notes |
lszczyrba@vt.edu |
Approved |
no |
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Call Number |
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Serial |
2218 |
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