A hybrid machine learning-mathematical programming optimization approach for municipal solid waste management during the pandemic
Author:
Funder
CONACyT
Publisher
Springer Science and Business Media LLC
Subject
Management, Monitoring, Policy and Law,Economics and Econometrics,Geography, Planning and Development
Link
https://link.springer.com/content/pdf/10.1007/s10668-023-03354-2.pdf
Reference56 articles.
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2. Ali, S. A., & Parvin, F. (2022). Examining challenges and multi-strategic approaches in waste management during the COVID-19 pandemic: A systematic review. Waste Management & Research, 40(9), 1356–1380. https://doi.org/10.1177/0734242X221079303
3. Andeobu, L., Wibowo, S., & Grandhi, S. (2022). Medical waste from COVID-19 pandemic—a systematic review of management and environmental impacts in Australia. International Journal of Environmental Research and Public Health, 19(3), 1381. https://doi.org/10.3390/ijerph19031381
4. Antonopoulos, I.-S., Karagiannidis, A., Tsatsarelis, T., & Perkoulidis, G. (2012). Applying waste management scenarios in the Peloponnese region in Greece: A critical analysis in the frame of life cycle assessment. Environmental Science and Pollution Research, 20(4), 2499–2511. https://doi.org/10.1007/s11356-012-1139-y
5. Azadi, S., & Karimi-Jashni, A. (2016). Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province. Iran. Waste Management, 48, 14–23. https://doi.org/10.1016/j.wasman.2015.09.034
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