Vulnerability-based regionalization for disaster management considering storms and earthquakes

Yenming J. Chen, Kuo-Hao Chang, Jiuh-Biing Sheu, Chih-Hao Liu, Chy-Chang Chang, Chieh-Hsin Chang and Guan-Xun Wang

Transportation Research Part E: Logistics and Transportation Review, 2023, vol. 169, issue C

Abstract: Existing approaches to risk assessment in natural disasters usually suffer from information specificity and data density. For example, road vulnerability assessment requires disaster information on the neighbors of all road segments, but weather stations may be far from such locations. The widely used intensity–duration–frequency analysis is too rough to overcome the tradeoff between estimation accuracy and reliability for assessing road vulnerability. Therefore, this study proposes a machine learning method to replace the frequency-style vulnerability with an intelligent measure, which will enhance the assessment by predicting the missing weather information at all road segments. Compared with conventional interpolation methods, our Markov chain random field kriging can reliably estimate the ungauged information when available stations are distributed asymmetrically. Therefore, our model contributes to the assessment methods by providing accurate and reliable vulnerability information for the transportation network under contemporary changing weather conditions. Through elaborate proofs to demonstrate the theoretical validity of our mathematical results, empirical results are also presented for application to practical governance. The empirical accuracy of 0.3 normalized root mean square error for disasters of storms and earthquakes is sufficient to convince local authorities to change their existing methods. The resulting regionalization in road network vulnerability is also tailor-made for practical applicability.

Keywords: Disaster management; Road resilience in disasters; Extreme rainfall; Ungauged locations; Markov chain random field (MCRF) kriging; Epidemic type aftershock sequence (ETAS); Percolation clustering; Vulnerability-based regionalization (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1366554522003647
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:transe:v:169:y:2023:i:c:s1366554522003647

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/bibliographic
http://www.elsevier. ... 600244/bibliographic

DOI: 10.1016/j.tre.2022.102987

Access Statistics for this article

Transportation Research Part E: Logistics and Transportation Review is currently edited by W. Talley

More articles in Transportation Research Part E: Logistics and Transportation Review from Elsevier
Bibliographic data for series maintained by Catherine Liu ().