The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Artificial intelligence applications in solid waste management: A systematic research review
Abstract
The waste management processes typically involve numerous technical, climatic, environmental, demographic, socio-economic, and legislative parameters. Such complex nonlinear processes are challenging to model, predict and optimize using conventional methods. Recently, artificial intelligence (AI) techniques have gained momentum in offering alternative computational approaches to solve solid waste management (SWM) problems. AI has been efficient at tackling ill-defined problems, learning from experience, and handling uncertainty and incomplete data. Although significant research was carried out in this domain, very few review studies have assessed the potential of AI in solving the diverse SWM problems. This systematic literature review compiled 85 research studies, published between 2004 and 2019, analyzing the application of AI in various SWM fields, including forecasting of waste characteristics, waste bin level detection, process parameters prediction, vehicle routing, and SWM planning. This review provides comprehensive analysis of the different AI models and techniques applied in SWM, application domains and reported performance parameters, as well as the software platforms used to implement such models. The challenges and insights of applying AI techniques in SWM are also discussed.
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Cites
Artificial intelligence for the modeling and control of combustion processes: a review
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Technologies and decision support systems to aid solid-waste management: a systematic review
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A Review on Prediction of Municipal Solid Waste Generation Models
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Cited by
Artificial intelligence for waste management in smart cities: a review
Fang, B., Yu, J., Chen, Z., Osman, A. I., Farghali, M., Ihara, I., Hamza, E. H., Rooney, D. W., & Yap, P.-S. (2023) · Environmental Chemistry Letters · doi:10.1007/s10311-023-01604-3 · Cited by 629
Kurniawan, T. A., Othman, M. H. D., Liang, X., Goh, H. H., Gikas, P., Kusworo, T. D., Anouzla, A., & Chew, K. W. (2023) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.117765 · Cited by 168
Zhou, J., Li, L., Wang, Q., Fan, Y. V., Liu, X., Klemeš, J. J., Wang, X., Tong, Y. W., & Jiang, P. (2022) · Waste Management · doi:10.1016/j.wasman.2022.03.029 · Cited by 49
Solano Meza, J. K., Orjuela Yepes, D., Rodrigo-Ilarri, J., & Rodrigo-Clavero, M.-E. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20054256 · Cited by 24
Metadata
- Authors
- Abdallah, M., Abu Talib, M., Feroz, S., Nasir, Q., Abdalla, H., & Mahfood, B.
- Venue
- Waste Management
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 109
- Pages
- 231-246
- ISSN
- 0956-053X (ISSN-L)
- OpenAlex
- W3024048126
- Cited by
- 523
- References
- 68
- FWCI
- 30.97
- Type
- journal article
- Language
- en
- Open access
- closed
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- Abdallah, M., Abu Talib, M., Feroz, S., Nasir, Q., Abdalla, H., & Mahfood, B. (2020). Artificial intelligence applications in solid waste management: A systematic research review. Waste Management. https://doi.org/10.1016/j.wasman.2020.04.057
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
