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Understanding municipal solid waste production and diversion factors utilizing deep-learning methods

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  • Zhao, Yidan
  • Li, Hong

Abstract

We propose a hybrid deep learning neural network model for accurately calculating municipal solid waste (MSW) production and diversions with socioeconomic and demographic factors across 220 municipalities of Ontario, Canada, from 2010 to 2021. The proposed SDAE model is constructed by layering multiple denoising autoencoders (DAEs) and Bootstrap aggregation (bagging). The proposed methodology is tested against benchmark ML techniques, including Decision Trees, ANNs, and SDEs. The findings indicate that deep learning techniques can develop solid waste models with high prediction accuracy. Moreover, it has been statistically confirmed that socioeconomic factors affect solid waste management.

Suggested Citation

  • Zhao, Yidan & Li, Hong, 2023. "Understanding municipal solid waste production and diversion factors utilizing deep-learning methods," Utilities Policy, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:juipol:v:83:y:2023:i:c:s0957178723001248
    DOI: 10.1016/j.jup.2023.101612
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    References listed on IDEAS

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    1. Hongda Liu & Haifeng Zhao, 2022. "Upgrading models, evolutionary mechanisms and vertical cases of service-oriented manufacturing in SVC leading enterprises: Product-development and service-innovation for industry 4.0," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-24, December.
    2. Xiuzhen, Xie & Zheng, Wenxiu & Umair, Muhammad, 2022. "Testing the fluctuations of oil resource price volatility: A hurdle for economic recovery," Resources Policy, Elsevier, vol. 79(C).
    3. Li, ChangZheng & Umair, Muhammad, 2023. "Does green finance development goals affects renewable energy in China," Renewable Energy, Elsevier, vol. 203(C), pages 898-905.
    4. Scott, James & Ho, William & Dey, Prasanta K. & Talluri, Srinivas, 2015. "A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments," International Journal of Production Economics, Elsevier, vol. 166(C), pages 226-237.
    5. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
    6. Liu, Hongda & Huang, Feipeng & Huang, Jialiang, 2022. "Measuring the coordination decision of renewable energy as a natural resource contracts based on rights structure and corporate social responsibility from economic recovery," Resources Policy, Elsevier, vol. 78(C).
    7. Sunghyeon Choi & Jin Hur, 2020. "An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting," Energies, MDPI, vol. 13(6), pages 1-16, March.
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