Machine learning approaches to modeling interdependent network restoration time
Author:
Funder
national science foundation
National Science Foundation
Publisher
Springer Science and Business Media LLC
Subject
General Environmental Science
Link
https://link.springer.com/content/pdf/10.1007/s10669-022-09882-y.pdf
Reference38 articles.
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2. Barabadi A, Ayele YZ (2018) Post-disaster infrastructure recovery: prediction of recovery rate using historical data. Reliab EngSyst Saf 169:209–223. https://doi.org/10.1016/j.ress.2017.08.018
3. Bell MGH, Kanturska U, Schmöcker JD, Fonzone A (2008) Attacker-defender models and road network vulnerability. Phil Trans R Soc A 366(1872):1893–1906. https://doi.org/10.1098/rsta.2008.0019
4. Breiman L (2001) Machine learning. Random For. https://doi.org/10.1201/9780429469275-8
5. Breusch TS, Pagan AR (1979) A simple test for heteroscedasticity and random coefficient variation. Econometrica 47(5):1287–1294
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1. Machine learning applications in the resilience of interdependent critical infrastructure systems—A systematic literature review;International Journal of Critical Infrastructure Protection;2024-03
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