Improving disasters preparedness and response for coastal communities using AIS ship tracking data

Samsul Islam, Floris Goerlandt, Xuran Feng, Mohammad Jasim Uddin, Yangyan Shi*, Casey Hilliard

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Many coastal communities are heavily dependent on maritime transportation for the ingress and egress of people and goods. Any major transportation disruption can have a significant negative impact on the safety, health and wellbeing of affected communities, this is due to the interruption in the availability of food and the supply medicines and fuel. Therefore, preparedness and the forward planning of an effective response are essential for successful emergency and recovery management. Accordingly, in this study, the concept of using AIS (Automatic Identification System) vessel tracking data has been applied for the study of disaster management in coastal communities. The AIS vessel tracking system has been an important development in navigational safety; this is because it continuously transmits important information to all other vessels about a particular vessel (including its position, identity, speed and route). One of the limitations of the AIS tracking system is that AIS data does not indicate commodity specifications: that is, the quantity of essential goods that each vessel is carrying to specified coastal communities. To overcome the limitation of the current AIS tracking system, we use an artificial neural network as an estimation tool. In the current study, AIS data are assessed and analyzed in addition to the augmentation of the capacity information of vessels; thus, the study develops a predictive model so that a relief manager can determine the actual needs of affected residents and thus be able to make responsible relief decisions (e.g., how much relief a disaster-affected community is likely to need). The study makes a unique contribution as its focus seeks to remedy the total lack of research on how to use AIS data in disaster operations.

Original languageEnglish
Article number101863
Pages (from-to)1-13
Number of pages13
JournalInternational Journal of Disaster Risk Reduction
Volume51
DOIs
Publication statusPublished - 1 Dec 2020

Keywords

  • AIS data
  • Coastal community
  • Disaster management
  • Disaster relief operations
  • Emergency management
  • Humanitarian logistics
  • Machine learning
  • Maritime risk
  • Maritime transportation
  • Neural network
  • Ship tracking data

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