Deep Learning Applications in Solid Waste Management: A Deep Literature Review

Solid waste management (SWM) has recently received more attention, especially in developing countries, for smart and sustainable development. SWM system encompasses various interconnected processes which contain numerous complex operations. Recently, deep learning (DL) has attained momentum in providing alternative computational techniques to determine the solution of various SWM problems. Researchers have focused on this domain; therefore, significant research has been published, especially in the last decade. The literature shows that no study evaluates the potential of DL to solve the various SWM problems. The study performs a systematic literature review (SLR) which has complied 40 studies published between 2019 and 2021 in reputed journals and conferences. The selected research studies have implemented the various DL models and analyzed the application of DL in different SWM areas, namely waste identification and segregation and prediction of waste generation. The study has defined the systematic review protocol that comprises various criteria and a quality assessment process to select the research studies for review. The review demonstrates the comprehensive analysis of different DL models and techniques implemented in SWM. It also highlights the application domains and compares the reported performance of selected studies. Based on the reviewed work, it can be concluded that DL exhibits the plausible performance to detect and classify the different types of waste. The study also explains the deep convolutional neural network with the computational requirement and determine the research gaps with future recommendations.

@article{Shahab2022,
  title     = {Deep Learning Applications in Solid Waste Management: A Deep Literature Review},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {3},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Sana Shahab and Mohd Anjum and M. Sarosh Umar},
  doi       = {10.14569/IJACSA.2022.0130347},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130347}
}