Systematic literature review of deep learning models in solid waste management

• Published in: AIP conference proceedings 2022-10, Vol.2494 (1)
• Main Authors: Anjum, Mohd, Umar, M. Sarosh, Shahab, Sana
• Format: Article
• Language: English
• Subjects: Deep learning; Developing countries; Domains; LDCs; Literature reviews; Quality assessment; Solid waste management; Solid wastes; Sustainable development; Systematic review
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0106304

Solid waste management (SWM) has received significantly more attention in recent years, especially in developing countries for 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. In the last few years, researchers have focused on this domain; therefore, significant research has been published. The literature shows that no study evaluates the potential of DL to solve the various SWM problems. The study performs a systematic literature review which has complied 25 studies, published between 2019 and 2021 in.reputed journals and conferences. The selected research studies have implemented the various DL models and analysed the application of DL in different SWM areas, namely waste identification and segregation, real-time bin level detection, 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 the different types of waste and bin level.