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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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