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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

Author

Listed:
  • Johanna Karina Solano Meza

    (Department of Environmental Engineering, Santo Tomás University, Road 9 Street 51-11, Bogotá 110231, Colombia)

  • David Orjuela Yepes

    (Department of Environmental Engineering, Santo Tomás University, Road 9 Street 51-11, Bogotá 110231, Colombia)

  • Javier Rodrigo-Ilarri

    (Instituto de Ingeniería del Agua y Medio Ambiente (IIAMA), Universitat Politècnica de València, 46022 Valencia, Spain)

  • María-Elena Rodrigo-Clavero

    (Instituto de Ingeniería del Agua y Medio Ambiente (IIAMA), Universitat Politècnica de València, 46022 Valencia, Spain)

Abstract

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.

Suggested Citation

  • Johanna Karina Solano Meza & David Orjuela Yepes & Javier Rodrigo-Ilarri & María-Elena Rodrigo-Clavero, 2023. "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," IJERPH, MDPI, vol. 20(5), pages 1-20, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:4256-:d:1082410
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    References listed on IDEAS

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    1. Johanna Karina Solano Meza & Javier Rodrigo-Ilarri & Claudia Patricia Romero Hernández & Mª Elena Rodrigo-Clavero, 2020. "Analytical Methodology for the Identification of Critical Zones on the Generation of Solid Waste in Large Urban Areas," IJERPH, MDPI, vol. 17(4), pages 1-14, February.
    2. Yao, Wanxiang & Zhang, Chunxiao & Hao, Haodong & Wang, Xiao & Li, Xianli, 2018. "A support vector machine approach to estimate global solar radiation with the influence of fog and haze," Renewable Energy, Elsevier, vol. 128(PA), pages 155-162.
    3. Jiayu Qiu & Bin Wang & Changjun Zhou, 2020. "Forecasting stock prices with long-short term memory neural network based on attention mechanism," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-15, January.
    4. Ane Pan & Linxiu Yu & Qing Yang, 2019. "Characteristics and Forecasting of Municipal Solid Waste Generation in China," Sustainability, MDPI, vol. 11(5), pages 1-11, March.
    5. Lu, Weisheng & Chen, Xi & Peng, Yi & Shen, Liyin, 2015. "Benchmarking construction waste management performance using big data," Resources, Conservation & Recycling, Elsevier, vol. 105(PA), pages 49-58.
    6. Dipti Singh & Ajay Satija, 2018. "Prediction of municipal solid waste generation for optimum planning and management with artificial neural network—case study: Faridabad City in Haryana State (India)," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 91-97, February.
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