Comparative Analysis of the Implementation of Support Vector Machines and Long Short-Term Memory Artificial Neural Networks in Municipal Solid Waste Management Models in Megacities
Open accessAbstract
The development of methodologies to support decision-making in municipal solid waste (MSW) management processes is of great interest for municipal administrations. Artificial intelligence (AI) techniques provide multiple tools for designing algorithms to objectively analyze data while creating highly precise models. Support vector machines and neuronal networks are formed by AI applications offering optimization solutions at different managing stages. In this paper, an implementation and comparison of the results obtained by two AI methods on a solid waste management problem is shown. Support vector machine (SVM) and long short-term memory (LSTM) network techniques have been used. The implementation of LSTM took into account different configurations, temporal filtering and annual calculations of solid waste collection periods. Results show that the SVM method properly fits selected data and yields consistent regression curves, even with very limited training data, leading to more accurate results than those obtained by the LSTM method.
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Unchecked AI use can encourage passive learning and cognitive offloading, particularly in environments where rapid completion of tasks is rewarded more strongly than reflective practice.
“This finding reinforces concerns that unchecked AI use can encourage passive learning and cognitive offloading, particularly in environments where rapid completion of tasks is rewarded more strongly than reflective practice [15, 16, 37].”
From From adoption to interaction: examining human-GenAI engagement patterns, ethical tensions, and educational trade-offs in nursing education (2026) · cites this paper as [15]
The Introduction and Discussion (pages not provided) address AI models for municipal solid waste management, not human learning. Searches for passive learning, cognitive offloading, and incentives for rapid task completion found no passage addressing the attributed concern. (LLM, conf=0.76)
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Yang, M., Long, J., He, Y., Meng, R., Ling, C., Long, Q., & Wen, J. (2026) · BMC Nursing · doi:10.1186/s12912-026-05132-7
- Authors
- Solano Meza, J. K., Orjuela Yepes, D., Rodrigo-Ilarri, J., & Rodrigo-Clavero, M.-E.
- Affiliations
- Department of Environmental Engineering, Santo Tomás University, Road 9 Street 51-11, Bogotá 110231, Colombia
- Universitat Politècnica de València (ES)
- Venue
- International Journal of Environmental Research and Public Health
- Publisher
- MDPI AG
- Published
- 2023-02-27
- Volume
- 20
- Issue
- 5
- Pages
- 4256
- ISSN
- 1660-4601 (ISSN-L)
- PMC
- PMC10002305
- OpenAlex
- W4322631256
- Cited by
- 24
- References
- 60
- Citations by year
- 2026: 9 · 2025: 9 · 2024: 5 · 2023: 1
- FWCI
- 4.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RiuNet (Universitat Politècnica de València) (submittedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Environmental Science › Waste Management and Disposal › Municipal Solid Waste Management
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- municipal solid waste management, support vector machine, long short-term memory networks, artificial neural networks, megacities, waste collection scheduling, regression prediction, model comparison
- MeSH terms
- Artificial Intelligence, Cities, Refuse Disposal, Neural Networks, Computer, Waste Management, Support Vector Machine, Solid Waste
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