Citation

Haggag M, Yorsi A, El-Dakhakhni W, Hassini E. Int. J. Disaster Risk Reduct. 2021; 56: e102121.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.ijdrr.2021.102121

PMID

unavailable

Abstract

The frequency of Climate-induced Disasters (CID) has tripled in the last three decades, driving the World Economic Forum to identify them as the most likely and most impactful risks worldwide. With more than 70% of the world population expected to be living in cities by 2050, ensuring the resilience of urban infrastructure systems under CID is crucial. The present work employs data analytics and machine learning techniques to develop a performance prediction framework for infrastructure systems under CID. The framework encompasses four stages related to: extracting meaningful information about the impact of CID on infrastructure systems and identifying the latter's performance; investigating the relationship between different CID attributes and previously identified system performance; employing data imputation using unsupervised machine learning techniques; and developing and testing a supervised machine learning model based on the different influencing CID attributes. To demonstrate its application, the developed framework is applied to disaster data compiled by the National Weather Services between 1996 and 2019 in the state of New York. The analysis results showed that: i) power systems in New York are the most vulnerable infrastructure to CID, and particularly to wind-related hazards; ii) power system performance level depends on hazard-system interactions rather than solely hazard characteristics; and iii) a 4-predictors random forest-based model can effectively predict power system performance with an accuracy of 89%. This work is expected to aid stakeholders in developing spatio-temporal preparedness plans under CID, which can facilitate mitigating the adverse impacts of CID on infrastructure systems and improve their resilience.

Language: en

Keywords

Climate-induced disasters; Data analytics; Infrastructure system; Machine learning; Resilience; Urban centres