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A Machine Learning Method to Quantify the Role of Vulnerability in Hurricane Damage
Laura Szczyrba
author
Yang Zhang
author
Duygu Pamukcu
author
Derya Ipek Eroglu
author
2020
Virginia Tech
Blacksburg, VA (USA)
English
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.
Vulnerability
Impact
Damage
Machine Learning
Hurricane María.
lszczyrba@vt.edu
exported from refbase (http://idl.iscram.org/show.php?record=2218), last updated on Mon, 29 Jun 2020 07:29:43 +0200
text
http://idl.iscram.org/files/lauraszczyrba/2020/2218_LauraSzczyrba_etal2020.pdf
LauraSzczyrba_etal2020
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management
Iscram 2020
Amanda Hughes
editor
Fiona McNeill
editor
Christopher W. Zobel
editor
17th International Conference on Information Systems for Crisis Response and Management
2020
Virginia Tech
Blacksburg, VA (USA)
conference publication
179
187
2411-3403
978-1-949373-27-17
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