Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices

Ling Tan (), Ji Guo (), Selvarajah Mohanarajah () and Kun Zhou ()
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Ling Tan: Nanjing University of Information Science and Technology
Ji Guo: Shanghai Maritime University
Selvarajah Mohanarajah: University of North Carolina At Pembroke
Kun Zhou: Nanjing University of Information Science & Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 107, issue 3, No 16, 2389-2417

Abstract: Abstract There has been an unsettling rise in the intensity and frequency of natural disasters due to climate change and anthropogenic activities. Artificial intelligence (AI) models have shown remarkable success and superiority to handle huge and nonlinear data owing to their higher accuracy and efficiency, making them perfect tools for disaster monitoring and management. Accordingly, natural disaster management (NDM) with the usage of AI models has received increasing attention in recent years, but there has been no systematic review so far. This paper presents a systematic review on how AI models are applied in different NDM stages based on 278 studies retrieved from Elsevier Science, Springer LINK and Web of Science. The review: (1) enables increased visibility into various disaster types in different NDM stages from the methodological and content perspective, (2) obtains many general results including the practicality and gaps of extant studies and (3) provides several recommendations to develop innovative AI models and improve the quality of modeling. Overall, a comprehensive assessment and evaluation for the reviewed studies are performed, which tracked all stages of NDM research with the applications of AI models.

Keywords: Natural disaster management; Artificial intelligence; Stage analysis (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11069-020-04429-3

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