Turning trash into treasure: exploring the potential of AI in municipal waste management - an in-depth review and future prospects
Rapid urbanization, economic expansion, and population growth have increased waste generation in many nations worldwide. Research on municipal waste management (MWM) is moving towards new frontiers in efficiency and applicability due to the growing amount of data being collected in these systems and...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
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Elsevier
2024
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| Online Access: | https://repository.londonmet.ac.uk/9910/3/Clean-Final%20Revised%20Manuscript-Accepted.pdf https://repository.londonmet.ac.uk/9910/9/1-s2.0-S0301479724036442-main.pdf |
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| author | El Jaouhari, Asmae Samadhiya, Ashutosh Kumar, Anil Mulat-Weldemeskel, Eyob Luthra, Sunil Kumar, Rajesh |
| author_facet | El Jaouhari, Asmae Samadhiya, Ashutosh Kumar, Anil Mulat-Weldemeskel, Eyob Luthra, Sunil Kumar, Rajesh |
| author_sort | El Jaouhari, Asmae |
| collection | LMU |
| description | Rapid urbanization, economic expansion, and population growth have increased waste generation in many nations worldwide. Research on municipal waste management (MWM) is moving towards new frontiers in efficiency and applicability due to the growing amount of data being collected in these systems and the convergence of various technological applications; artificial intelligence (AI) techniques present novel and creative alternatives for MWM. Even though much research has been conducted in this field, relatively few review studies assess how advancements in AI techniques can contribute to the sustainable advancement of MWM systems. Furthermore, there are discrepancies and a dearth of knowledge regarding the operation of AI-based techniques in MWM. To close this gap, this study conducts a thorough review of the relevant literature with an application of preferred reporting items for systematic reviews and meta-analyses-based methods, examining 229 peer-reviewed publications to explore the role of AI in different MWM areas, such as waste characteristics forecasting, waste bin level monitoring, process parameter prediction, vehicle routing, and MWM planning. The main AI techniques and models used in MWM optimization, as well as the application areas and stated performance metrics, are all thoroughly analyzed in this review. A conceptual framework is proposed to guide research and practice to take a holistic approach to MWM, along with areas of future study that need to be explored. Researchers, policymakers, municipalities, governments, and other waste management organizations will benefit from this study to minimize costs, maximize efficiency, eliminate the need for manual labor, and change the approach to MWM. |
| first_indexed | 2025-02-19T01:16:22Z |
| format | Article |
| id | oai:repository.londonmet.ac.uk:9910 |
| institution | London Metropolitan University |
| language | English English |
| last_indexed | 2025-02-19T01:16:22Z |
| publishDate | 2024 |
| publisher | Elsevier |
| record_format | eprints |
| spelling | oai:repository.londonmet.ac.uk:99102025-01-02T12:38:23Z https://repository.londonmet.ac.uk/9910/ Turning trash into treasure: exploring the potential of AI in municipal waste management - an in-depth review and future prospects El Jaouhari, Asmae Samadhiya, Ashutosh Kumar, Anil Mulat-Weldemeskel, Eyob Luthra, Sunil Kumar, Rajesh 000 Computer science, information & general works 600 Technology 650 Management & auxiliary services Rapid urbanization, economic expansion, and population growth have increased waste generation in many nations worldwide. Research on municipal waste management (MWM) is moving towards new frontiers in efficiency and applicability due to the growing amount of data being collected in these systems and the convergence of various technological applications; artificial intelligence (AI) techniques present novel and creative alternatives for MWM. Even though much research has been conducted in this field, relatively few review studies assess how advancements in AI techniques can contribute to the sustainable advancement of MWM systems. Furthermore, there are discrepancies and a dearth of knowledge regarding the operation of AI-based techniques in MWM. To close this gap, this study conducts a thorough review of the relevant literature with an application of preferred reporting items for systematic reviews and meta-analyses-based methods, examining 229 peer-reviewed publications to explore the role of AI in different MWM areas, such as waste characteristics forecasting, waste bin level monitoring, process parameter prediction, vehicle routing, and MWM planning. The main AI techniques and models used in MWM optimization, as well as the application areas and stated performance metrics, are all thoroughly analyzed in this review. A conceptual framework is proposed to guide research and practice to take a holistic approach to MWM, along with areas of future study that need to be explored. Researchers, policymakers, municipalities, governments, and other waste management organizations will benefit from this study to minimize costs, maximize efficiency, eliminate the need for manual labor, and change the approach to MWM. Elsevier 2024-12-09 Article PeerReviewed text en cc_by_nc_nd_4 https://repository.londonmet.ac.uk/9910/3/Clean-Final%20Revised%20Manuscript-Accepted.pdf text en cc_by_nc_nd_4 https://repository.londonmet.ac.uk/9910/9/1-s2.0-S0301479724036442-main.pdf El Jaouhari, Asmae, Samadhiya, Ashutosh, Kumar, Anil, Mulat-Weldemeskel, Eyob, Luthra, Sunil and Kumar, Rajesh (2024) Turning trash into treasure: exploring the potential of AI in municipal waste management - an in-depth review and future prospects. Journal of Environmental Management, 373 (123658). pp. 1-17. ISSN 0301-4797 https://doi.org/10.1016/j.jenvman.2024.123658 10.1016/j.jenvman.2024.123658 10.1016/j.jenvman.2024.123658 |
| spellingShingle | 000 Computer science, information & general works 600 Technology 650 Management & auxiliary services El Jaouhari, Asmae Samadhiya, Ashutosh Kumar, Anil Mulat-Weldemeskel, Eyob Luthra, Sunil Kumar, Rajesh Turning trash into treasure: exploring the potential of AI in municipal waste management - an in-depth review and future prospects |
| title | Turning trash into treasure: exploring the potential of AI in municipal waste management - an in-depth review and future prospects |
| title_full | Turning trash into treasure: exploring the potential of AI in municipal waste management - an in-depth review and future prospects |
| title_fullStr | Turning trash into treasure: exploring the potential of AI in municipal waste management - an in-depth review and future prospects |
| title_full_unstemmed | Turning trash into treasure: exploring the potential of AI in municipal waste management - an in-depth review and future prospects |
| title_short | Turning trash into treasure: exploring the potential of AI in municipal waste management - an in-depth review and future prospects |
| title_sort | turning trash into treasure exploring the potential of ai in municipal waste management an in depth review and future prospects |
| topic | 000 Computer science, information & general works 600 Technology 650 Management & auxiliary services |
| url | https://repository.londonmet.ac.uk/9910/3/Clean-Final%20Revised%20Manuscript-Accepted.pdf https://repository.londonmet.ac.uk/9910/9/1-s2.0-S0301479724036442-main.pdf |
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