Skip to main content
Springer Nature Link
Log in

Trends and Developments in the Use of Machine Learning for Disaster Management: A Bibliometric Analysis

  • Conference paper
  • First Online:
Transfer, Diffusion and Adoption of Next-Generation Digital Technologies (TDIT 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 698))

Included in the following conference series:

  • 774 Accesses

Abstract

The frequency of the occurrence of disasters, and the severity of their effects have both been significantly rising over the past few decades across the world. Recognizing the potential of Artificial Intelligence (AI), particularly its subset, Machine Learning (ML), this study delves into its application in disaster management. More specifically, this study adopted the bibliometric analysis methodology to examine the most active authors, countries, and institutions in research related to the use of ML in disaster management and to investigate the trending themes associated with ML use in disaster management. Based on the results, it can be concluded that the citation networks demonstrate the close collaboration between the USA, India, China, and Australia. India had the most articles cited with 1672 citations, despite China having the largest production of research related to the use of ML in disaster management. Furthermore, besides “disaster management” and “machine learning” which were expected to be part of the key drivers in this research area, “remote sensing” also emerged as a trending topic. Based on the thematic analysis of the various articles retrieved in this study, future research must include fourth industrial revolution (4IR) technologies, as they are crucial to disaster management.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Kankanamge, N., Yigitcanlar, T., Goonetilleke, A.: Public perceptions on artificial intelligence driven disaster management: evidence from Sydney, Melbourne and Brisbane. Telemat. Inform. 65, 101729 (2021). https://doi.org/10.1016/j.tele.2021.101729

  2. Munawar, H.S., Mojtahedi, M., Hammad, A.W.A., Kouzani, A., Mahmud, M.A.P.: Disruptive technologies as a solution for disaster risk management: a review. Sci. Total Environ. 806 (2022). https://doi.org/10.1016/j.scitotenv.2021.151351

  3. Prasad, R., Udeme, A.U., Misra, S., Bisallah, H.: Identification and classification of transportation disaster tweets using improved bidirectional encoder representations from transformers. Int. J. Inf. Manag. Data Insights 3(1), 1–8 (2023). https://doi.org/10.1016/j.jjimei.2023.100154

    Article  Google Scholar 

  4. Powers, C.J., et al.: Using artificial intelligence to identify emergency messages on social media during a natural disaster: a deep learning approach. Int. J. Inf. Manag. Data Insights 3(1), 100164 (2023). https://doi.org/10.1016/j.jjimei.2023.100164

    Article  Google Scholar 

  5. Farazmehr, S., Wu, Y.: Locating and deploying essential goods and equipment in disasters using AI-enabled approaches: a systematic literature review. Prog. Disaster Sci. 19, 100292 (2023). https://doi.org/10.1016/j.pdisas.2023.100292

  6. Chen, Y.J., et al.: Vulnerability-based regionalization for disaster management considering storms and earthquakes. Transp. Res. Part E Logist. Transp. Rev. 169, 102987 (2023). https://doi.org/10.1016/j.tre.2022.102987

  7. Enns, C., Bersaglio, B., Karmushu, R.: Disaster management takes to the skies: how new technologies are reconfiguring spatialities of power in desert locust management. Polit. Geogr. 98, 102732 (2022). https://doi.org/10.1016/j.polgeo.2022.102732

    Article  Google Scholar 

  8. Munawar, H.S., Hammad, A.W.A., Waller, S.T.: A review on flood management technologies related to image processing and machine learning. Autom. Constr. 132, 103916 (2021). https://doi.org/10.1016/j.autcon.2021.103916

  9. Habibi, A., Delavar, M.R., Sadeghian, M.S., Nazari, B., Pirasteh, S.: A hybrid of ensemble machine learning models with RFE and Boruta wrapper-based algorithms for flash flood susceptibility assessment. Int. J. Appl. Earth Obs. Geoinf. 122, 103401 (2023). https://doi.org/10.1016/j.jag.2023.103401

  10. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., Lim, W.M.: How to conduct a bibliometric analysis: an overview and guidelines. J. Bus. Res. 133, 285–296 (2021). https://doi.org/10.1016/j.jbusres.2021.04.070

    Article  Google Scholar 

  11. Uğur, N.G., Akbıyık, A.: Emerging trends in IS research: a co-word analysis (2007–16)/Tendances émergentes en recherche en systèmes d’information: une analyse de cooccurrents (2007–16). Can. J. Inf. Libr. Sci. 42, 228–248 (2018). https://muse.jhu.edu/article/743054/summary

  12. Chen, X., Chen, J., Wu, D., Xie, Y., Li, J.: Mapping the research trends by co-word analysis based on keywords from funded project. Procedia Comput. Sci. 91, 547–555 (2016). Itqm. https://doi.org/10.1016/j.procs.2016.07.140

  13. Dervis, H.: Bibliometric analysis using bibliometrix an R package. J. Scientometr. Res. 8(3), 156–160 (2019). https://doi.org/10.5530/JSCIRES.8.3.32

    Article  Google Scholar 

  14. Lin, T.C., Tang, K.Y., Lin, S.S., Changlai, M.L., Hsu, Y.S.: A co-word analysis of selected science education literature: identifying research trends of scaffolding in two decades (2000–2019). Front. Psychol. 13, 1–14 (2022). https://doi.org/10.3389/fpsyg.2022.844425

    Article  Google Scholar 

  15. Iqbal, U., Perez, P., Barthelemy, J.: A process-driven and need-oriented framework for review of technological contributions to disaster management. Heliyon 7(11), e08405 (2021). https://doi.org/10.1016/j.heliyon.2021.e08405

    Article  Google Scholar 

  16. Berawi, M.A., et al.: Increasing disaster victim survival rate: SaveMyLife mobile application development. Int. J. Disaster Risk Reduct. 60 (2021). https://doi.org/10.1016/j.ijdrr.2021.102290

  17. Guha, S., Jana, R.K., Sanyal, M.K.: Artificial neural network approaches for disaster management: a literature review. Int. J. Disaster Risk Reduct. 81, 103276 (2022). https://doi.org/10.1016/j.ijdrr.2022.103276

  18. Vahdani, B.: A flexible framework to coordinate debris clearance and relief distribution operations: a robust machine learning approach. Expert Syst. Appl. 229(PA), 120512 (2023). https://doi.org/10.1016/j.eswa.2023.120512

  19. Achu, A.L., et al.: Machine-learning based landslide susceptibility modeling with emphasis on uncertainty analysis. Geosci. Front. 14(6), 101657 (2023). https://doi.org/10.1016/j.gsf.2023.101657

    Article  Google Scholar 

  20. Chikaraishi, M., et al.: On the possibility of short-term traffic prediction during disaster with machine learning approaches: an exploratory analysis. Transp. Policy 98, 91–104 (2020). https://doi.org/10.1016/j.tranpol.2020.05.023

    Article  Google Scholar 

  21. Zhang, S., et al.: Estimating the grade of storm surge disaster loss in coastal areas of China via machine learning algorithms. Ecol. Indic. 136, 108533 (2022). https://doi.org/10.1016/j.ecolind.2022.108533

    Article  Google Scholar 

  22. Saleem, F., Khattak, A., Ur Rehman, S., Ashiq, M.: Bibliometric analysis of green marketing research from 1977 to 2020. Publications 9(1), 1–19 (2021). https://doi.org/10.3390/publications9010001

  23. Luo, X., Wu, Y., Niu, L., Huang, L.: Bibliometric analysis of health technology research: 1990~2020. Int. J. Environ. Res. Public Health 19(15) (2022). https://doi.org/10.3390/ijerph19159044

  24. Wang, C., Si, L.: A bibliometric analysis of digital literacy research from 1990 to 2022 and research on emerging themes during the COVID-19 pandemic. Sustainability 15(7) (2023).https://doi.org/10.3390/su15075769

  25. Chamola, V., Hassija, V., Gupta, V., Guizani, M.: A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact. IEEE Access, 90225–90265 (2020)

    Google Scholar 

  26. Muhammad, K., Ahmad, J., Baik, S.W.: Early fire detection using convolutional neural networks during surveillance for effective disaster management. Neurocomputing 288, 30–42 (2018). https://doi.org/10.1016/j.neucom.2017.04.083

    Article  Google Scholar 

  27. Agbo, F.J., Oyelere, S.S., Suhonen, J., Tukiainen, M.: Scientific production and thematic breakthroughs in smart learning environments: a bibliometric analysis. Smart Learn. Environ. 8(1), 1–25 (2021)

    Article  Google Scholar 

  28. Fosso Wamba, S., Bawack, R.E., Guthrie, C., Queiroz, M.M., Carillo, K.D.A.: Are we preparing for a good AI society? A bibliometric review and research agenda. Technol. Forecast. Soc. Change 164 (2021). https://doi.org/10.1016/j.techfore.2020.120482

  29. Wang, X., Lu, J., Song, Z., Zhou, Y.: From past to future: bibliometric analysis of global research productivity on nomogram (2000–2021). Front. Public Health 10, 1–15 (2022). https://doi.org/10.3389/fpubh.2022.997713

    Article  Google Scholar 

  30. Shen, G., Zhou, L., Xue, X., Zhou, Y.: The risk impacts of global natural and technological disasters. Socioecon. Plann. Sci. 88, 1–16 (2023). https://doi.org/10.1016/j.seps.2023.101653

    Article  Google Scholar 

  31. Rasool, U., et al.: Rainfall-driven machine learning models for accurate flood inundation mapping in Karachi, Pakistan. Urban Clim. 49, 101573 (2023). https://doi.org/10.1016/j.uclim.2023.101573

  32. Vishwanath, T., Shirwaikar, R.D., Jaiswal, W.M., Yashaswini, M.: Social media data extraction for disaster management aid using deep learning techniques. Remote Sens. Appl. Soc. Environ. 30, 100961 (2023). https://doi.org/10.1016/j.rsase.2023.100961

  33. Du, X., et al.: High-accuracy estimation method of typhoon storm surge disaster loss under small sample conditions by information diffusion model coupled with machine learning models. Int. J. Disaster Risk Reduct. 82, 103307 (2022). https://doi.org/10.1016/j.ijdrr.2022.103307

  34. Shahabi, H., et al.: Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: hybrid intelligence of bagging ensemble based on K-Nearest Neighbor classifier. Remote Sens. 12(2), 1–30 (2020)

    Article  Google Scholar 

  35. Zhou, X., et al.: Zonation of landslide susceptibility in Ruijin, Jiangxi, China. Int. J. Environ. Res. Public Health 18(11) (2021). https://doi.org/10.3390/ijerph18115906

  36. Razavi-Termeh, S.V., Sadeghi-Niaraki, A., Seo, M.B., Choi, S.M.: Application of genetic algorithm in optimization parallel ensemble-based machine learning algorithms to flood susceptibility mapping using radar satellite imagery. Sci. Total Environ. 873, 162285 (2023). https://doi.org/10.1016/j.scitotenv.2023.162285

Download references

Author information

Authors and Affiliations

  1. Department of Applied Information Systems, University of Johannesburg, Bunting Road Campus, Auckland Park, Johannesburg, 2092, South Africa

    Kudakwashe Maguraushe, Patrick Ndayizigamiye & Tebogo Bokaba

Authors
  1. Kudakwashe Maguraushe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patrick Ndayizigamiye
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tebogo Bokaba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Patrick Ndayizigamiye .

Editor information

Editors and Affiliations

  1. IIM Nagpur, Nagpur, India

    Sujeet K. Sharma

  2. Swansea University, Swansea, UK

    Yogesh K. Dwivedi

  3. IIM Nagpur, Nagpur, India

    Bhimaraya Metri

  4. University of Bradford, Bradford, UK

    Banita Lal

  5. Royal Holloway, University of London, Egham, UK

    Amany Elbanna

Rights and permissions

Reprints and permissions

Copyright information

© 2024 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Maguraushe, K., Ndayizigamiye, P., Bokaba, T. (2024). Trends and Developments in the Use of Machine Learning for Disaster Management: A Bibliometric Analysis. In: Sharma, S.K., Dwivedi, Y.K., Metri, B., Lal, B., Elbanna, A. (eds) Transfer, Diffusion and Adoption of Next-Generation Digital Technologies. TDIT 2023. IFIP Advances in Information and Communication Technology, vol 698. Springer, Cham. https://doi.org/10.1007/978-3-031-50192-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50192-0_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50191-3

  • Online ISBN: 978-3-031-50192-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships