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

Author

Listed:
  • Ling Tan

    (Nanjing University of Information Science and Technology)

  • Ji Guo

    (Shanghai Maritime University
    Nanjing University of Information Science and Technology)

  • Selvarajah Mohanarajah

    (University of North Carolina At Pembroke)

  • Kun Zhou

    (Nanjing University of Information Science & Technology)

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.

Suggested Citation

  • Ling Tan & Ji Guo & Selvarajah Mohanarajah & Kun Zhou, 2021. "Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2389-2417, July.
    • Handle: RePEc:spr:nathaz:v:107:y:2021:i:3:d:10.1007_s11069-020-04429-3
    DOI: 10.1007/s11069-020-04429-3
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    3. Jie Gao & Wu Zhang & Chunbaixue Yang & Rui Wang & Shuai Shao & Jiawei Li & Limiao Zhang & Zhijian Li & Shu Liu & Wentao Si, 2022. "Comparative Study on International Research Hotspots and National-Level Policy Keywords of Dynamic Disaster Monitoring and Early Warning in China (2000–2021)," IJERPH, MDPI, vol. 19(22), pages 1-19, November.
    4. Xiaoli Wang & Yun Liu & Lingdi Chen & Yifan Zhang, 2022. "Correlation Monitoring Method and model of Science-Technology-Industry in the AI Field: A Case of the Neural Network," SAGE Open, , vol. 12(4), pages 21582440221, December.
    5. Farzaneh Mohammadi Jouzdani & Vahid Javidroozi & Hanifa Shah & Monica Mateo Garcia, 2025. "A Systemic Digital Transformation for Smart Net-Zero Cities: A State-of-the-Art Review," J, MDPI, vol. 8(1), pages 1-32, March.
    6. Claudia Calle Müller & Mohamed ElZomor, 2024. "Addressing Post-Disaster Challenges and Fostering Social Mobility through Origami Infrastructure and Construction Trade Education," Sustainability, MDPI, vol. 16(8), pages 1-17, April.
    7. Claudia Calle Müller & Leonel Lagos & Mohamed Elzomor, 2024. "Leveraging Disruptive Technologies for Faster and More Efficient Disaster Response Management," Sustainability, MDPI, vol. 16(23), pages 1-38, December.

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