Smart-Waste-Management-System - Harshit Thakkar; Jeevottam Heble; Dr. Rohini S. Hallikar (International Journal of Environmental Noise and Pollution Control, 2025)
The rapid urbanization and increasing waste generation pose significant challenges to traditional waste management systems, necessitating innovative solutions that integrate economic principles and management strategies. In order to enhance trash transportation and recycling procedures, this paper investigates the deployment of a Smart trash Management System that makes use of Internet of Things (IoT) components and machine learning algorithms. By applying economic principles such as cost-benefit analysis and resource allocation, and management strategies like strategic planning and operational efficiency, the system aims to predict the filling levels of recycling containers, thereby reducing unnecessary transportation and ensuring timely emptying. To increase the system's accuracy and efficiency, a number of approaches are assessed, including conventional machine learning methods like Random Forest, K-nearest neighbors, Linear Regression, Support Vector Machine, and Artificial Neural Networks. According to the results, the best-performing Random Forest classifier improves the quality of predictions for recycling container emptying times by boosting recall by 50.3% and accuracy by 12.3%. Along with suggestions for additional study and other system enhancements, the findings' implications for future waste management tactics are examined. Policymakers, waste management firms, and researchers can all benefit from this study's thorough examination of the possible advantages and difficulties of integrating IoT and machine learning technology in trash management.
Keywords: IoT in waste management; Machine Learning; Smart waste management; automated waste collection; fill-level detection; predictive analytics; route optimization algorithms; smart bin technology; ultrasonic sensors; waste composition analysis; waste management infrastructure