AI-Powered Waste Management: A Machine Learning-Based Approach to Sustainability
Abstract
The increasing global waste crisis necessitates innovative solutions for efficient waste management. This paper investigates the application of machine learning in automating waste classification, optimizing recycling processes, and reducing landfill waste. ML techniques such as convolutional neural networks (CNNs) and support vector machines (SVMs) are employed to classify waste types accurately. Additionally, reinforcement learning models are explored for optimizing waste collection routes, reducing fuel consumption, and minimizing emissions. A case study on AI-driven smart waste management systems in urban environments is presented, demonstrating significant improvements in recycling rates and environmental impact mitigation.
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