Smart-Waste-Management-System

Year : 2025 | Volume : 03 | Issue : 02 | Page : 18 27
    By

    Harshit Thakkar,

  • Jeevottam Heble,

  • Dr. Rohini S. Hallikar,

  1. Assis. Prof., Department of Electronics and Communication Engineering RV College of Engineering® Bengaluru, Karnataka, India
  2. Assis. Prof., Department of Electronics and Communication Engineering RV College of Engineering® Bengaluru, Karnataka, India
  3. Assis. Prof., Department of Electronics and Communication Engineering RV College of Engineering® Bengaluru, Karnataka, India

Abstract

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: Smart waste management, IoT in waste management, ultrasonic sensors, fill-level detection, route optimization algorithms, machine learning, predictive analytics, waste composition analysis, automated waste collection, smart bin technology, waste management infrastructure

[This article belongs to International Journal of Environmental Noise and Pollution Control ]

How to cite this article:
Harshit Thakkar, Jeevottam Heble, Dr. Rohini S. Hallikar. Smart-Waste-Management-System. International Journal of Environmental Noise and Pollution Control. 2025; 03(02):18-27.
How to cite this URL:
Harshit Thakkar, Jeevottam Heble, Dr. Rohini S. Hallikar. Smart-Waste-Management-System. International Journal of Environmental Noise and Pollution Control. 2025; 03(02):18-27. Available from: https://journals.stmjournals.com/ijenpc/article=2025/view=230866


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[12] S. M. Alavi, A. H. Gandomi, and L. Abualigah, “Machine learning techniques in waste management: A comprehensive review,” Journal of Cleaner Production, vol. 280, p. 124345, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S095965262034345X

[13] S. Kaza, L. Yao, P. Bhada-Tata, and F. Van Woerden, “What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050,” World Bank, 2018. [Online]. Available: https://openknowledge.worldbank.org/handle/10986/30317

[14] S. K. Ghosh and R. Shah, “Smart waste management system using IoT and machine learning,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 3, pp. 1093-1105, 2020. [Online]. Available: https://link.springer.com/article/10.1007/s12652-019-01234-5

[15] A. Belhadi, F. Touriki, and A. Elmosbahi, “Optimization of waste collection routes using IoT and machine learning,” IEEE Access, vol. 9, pp. 50000-50012, 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9372111

[16] L. Wang, X. Li, and Y. Zhang, “IoT and Machine Learning in Smart Waste Management: A Review,” IEEE Internet of Things Journal, vol. 7, no. 5, pp. 3852-3865, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/9055454

[17] S. Kaza, L. Yao, P. Bhada-Tata, and F. Van Woerden, “What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050,” World Bank, 2018. [Online]. Available: https://openknowledge.worldbank.org/handle/10986/30317

[18] S. M. Alavi, A. H. Gandomi, and L. Abualigah, “Machine learning techniques in waste management: A comprehensive review,” Journal of Cleaner Production, vol. 280, p. 124345, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S095965262034345X

[19] S. K. Ghosh and R. Shah, “Smart waste management system using IoT and machine learning,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 3, pp. 1093-1105, 2020. [Online]. Available: https://link.springer.com/article/10.1007/s12652-019-01234-5

[20] A. Belhadi, F. Touriki, and A. Elmosbahi, “Optimization of waste collection routes using IoT and machine learning,” IEEE Access, vol. 9, pp. 50000-50012, 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9372111

Regular Issue Subscription Original Research
Volume 03
Issue 02
Received 04/07/2025
Accepted 19/09/2025
Published 04/11/2025
Publication Time 123 Days
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