A Bayesian Network risk model for estimating coastal maritime transportation delays following an earthquake in British Columbia

Floris Goerlandt and Samsul Islam

Reliability Engineering and System Safety, 2021, vol. 214, issue C

Abstract: Coastal communities are vulnerable to the consequences of disasters such as hurricanes, earthquakes and tsunamis. Often, such communities are heavily dependent on maritime transportation for the ingress of essential goods such as food, fuel, and medicine. Major natural disasters can cause damage to critical infrastructures, causing disruptions to logistics chains and delays in the delivery of essential goods to vulnerable coastal communities. Estimating such delays is an important aspect of disaster preparedness planning, as it can support community-focused risk mitigating policies, improve emergency response operations, and help identify resilience strategies. In this article, a Bayesian Network risk model is developed for estimating the delays in maritime transportation to island communities in British Columbia, resulting from a major earthquake in the region. The model takes a regional scope and is primarily expert-driven. Correspondingly, it uses information about the earthquake intensity, infrastructure damages, impacts on shipping operations, and community needs to estimate delay times of the operability of different shipping services in the region under various scenarios. The model is illustrated through a series of hypothetical scenarios. Various validation tests furthermore indicate an adequate model performance for supporting regional disaster preparedness planning for the immediate response phase following an earthquake.

Keywords: Maritime transportation; Bayesian Network; Humanitarian logistics; Logistics disruption; Natural hazards; NaTech events (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:214:y:2021:i:c:s095183202100243x

DOI: 10.1016/j.ress.2021.107708

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