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Author (up) Laura Szczyrba; Yang Zhang; Duygu Pamukcu; Derya Ipek Eroglu pdf  isbn
  Title A Machine Learning Method to Quantify the Role of Vulnerability in Hurricane Damage Type Conference Article
  Year 2020 Publication ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 179-187  
  Keywords Vulnerability, Impact, Damage, Machine Learning, Hurricane María.  
  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.  
  Address Virginia Tech; Virginia Tech; Virginia Tech; Virginia Tech  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-17 ISBN 2411-3403 Medium  
  Track Analytical Modeling and Simulation Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 2218  
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