Machine Learning and IoT-based Solid Waste Management: Revolutionizing Efficiency and Sustainability for a Clean Environment

he research article titled “Machine Learning and IoT-Based Solid Waste Management: Revolutionizing Efficiency and Sustainability for a Clean Environment” explores a pioneering approach to urban waste management. This study leverages the synergy of Internet of Things (IoT) technology and machine learning to optimize the collection and disposal of solid waste. A network of sensors, including ultrasonic, infrared, and weight sensors, is strategically deployed to monitor waste bin fill levels in real-time. The sensor data is then processed by machine learning models, namely Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Random Forest (RF), to predict bin fill levels accurately. The results demonstrate exceptional predictive performance, with ANN achieving an accuracy rate of 98.3%, followed closely by LSTM at 95.5%, and RF at 93.55%. These models provide actionable insights into waste accumulation trends, enabling municipalities and waste management authorities to proactively plan collection schedules and allocate resources efficiently. Moreover, the system’s precision, recall, and F1 score metrics validate its effectiveness in identifying filled bins, reducing unnecessary collections, minimizing environmental impact, and optimizing operational costs.