A Machine Learning Approach for Smart Waste Management Systems that is Automated

Abstract

A waste management system is the concept in an organization that is used to dispose, reduce, reuse, and prevent waste. Some of the waste disposal methods are recycling, composting, incineration, landfills, bioremediation, waste of energy, and waste minimization. Traditional waste management system operates based on daily schedule which is highly inefficient and costly. Numerous data-driven methods for solving the problem are investigated in a realistic setting where most of the events are not actual emptying. Waste management is a daily task in urban areas, which requires a large amount of labour resources and affects natural, budgetary, efficiency, and social aspects. Many approaches have been proposed to optimize waste management, such as using the nearest neighbour search, colony optimization, genetic algorithm, and particle swarm optimization methods. The isolation of waste is done by unskilled workers which are less effective, time-consuming, and not plausible because of a lot of waste. So, proposing an automated waste classification problem utilizing Machine Learning algorithms. The use of machine learning allows improving the classification accuracy and recall of the existing manually engineered model.