A Conceptualized Study of ML Classifiers in Solid Waste Management
Prabha Shreeraj Nair, Akanksha Srivastav, Mir Aadil · 2024
The phenomenon of population development and the subsequent acceleration of urbanization has resulted in a significant upsurge in the generation of municipal solid garbage. Consequently, scholars and experts have endeavoured to employ sophisticated technology in order to address this pressing issue. In recent years, there has been a progressive integration of machine learning (ML) algorithms into the field of municipal solid waste management (MSWM) with the aim of facilitating sustainable environmental growth. ML algorithms have demonstrated their effectiveness in modelling intricate nonlinear systems. This study involved the assessment and analysis of over 200 papers that were published throughout the timeframe of two decades, namely from 2016 to 2023. This study provides a comprehensive overview of the utilization of machine learning algorithms throughout the entirety of MSWM, encompassing trash creation, collection and transportation, and ultimate disposal stages. This thorough study aims to address the gaps and identify future directions in the application of ML in MSWM. The findings of this review offer both theoretical and practical guidelines for future research in this field.