Trends and Developments in the Use of Machine Learning for Disaster Management: A Bibliometric Analysis
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50192-0_9
Reference36 articles.
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
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
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
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