Natural Language Processing for Infrastructure Resilience to Natural Disasters: A Scientometric Review
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1972-3_165
Reference16 articles.
1. Fan, C., Mostafavi, A.: A graph-based method for social sensing of infrastructure disruptions in disasters. Comput.-Aided Civil Infrastruct. Eng. 34, 1055–1070 (2019). https://doi.org/10.1111/mice.12457
2. Heglund, J., Hopkinson, K.M., Tran, H.T.: Social sensing: towards social media as a sensor for resilience in power systems and other critical infrastructures. Sustain. Resilient Infrastruct. 6, 94–106 (2021). https://doi.org/10.1080/23789689.2020.1719728
3. Bai, Y., Wu, J., Yuan, S., Reniers, G., Yang, M., Cai, J.: Dynamic resilience assessment and emergency strategy optimization of natural gas compartments in utility tunnels. Process. Saf. Environ. Prot. 165, 114–125 (2022). https://doi.org/10.1016/j.psep.2022.07.008
4. Gruebner, O., Lowe, S.R., Sykora, M., Shankardass, K., Subramanian, S.V., Galea, S.: Spatio-Temporal distribution of negative emotions in New York City after a natural disaster as seen in social media. Int. J. Environ. Res. Public Health 15, 2275 (2018). https://doi.org/10.3390/ijerph15102275
5. Fan, C., Farahmand, H., Mostafavi, A.: Rethinking infrastructure resilience assessment with human sentiment reactions on social media in disasters. In: Proceedings of the Hawaii International Conference on System Sciences, Hawaii, USA, January 2020. https://doi.org/10.24251/HICSS.2020.205
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