Municipal Solid Waste Management: A Review of Machine Learning Applications

Abstract

This study comprises of an analysis of various Machine Learning (ML) algorithms for municipal solid waste management to enhance waste management procedures and reduce the adverse environmental effects. The increasing population has resulted in substantial environmental hazards due to increased waste generation. Therefore, an effective waste management system with much more efficient and innovative waste management techniques is required to reduce the adverse effects that would occur due to the generation of massive waste. This study reviews various ML algorithms to automate and optimize garbage generation, collection, transportation, treatment, and disposal. To deliver and predict effective and precise waste generation, segregation, and collection forecasts, the system integrates multiple ML methods including decision trees (DT), k-nearest neighbours (KNN), support vector machines (SVM), random forests (RF), and clustering algorithms.