Dynamic Waste Management Optimization Using LSTM-Based Predictive Analytics and Robotic Sorting Technologies

• Published in: Communications and Signal Processing, International Conference on pp. 1224 - 1229
• Main Authors: Annamalai, Perumal, K, Saravanan, K.R, Jansi, A, Latha, M, Muthulekshmi, G, Padma Malini
• Format: Conference Proceeding
• Language: English
• Published: IEEE 05.06.2025
• Subjects: Biological system modeling; Data Analysis; Data models; Garbage Management; Internet of Things; Long short term memory; Long Short-term Memory Model; Predictive analytics; Predictive models; Real-time systems; Robotic Sorting; Sensors; Sorting; Waste management
• ISSN: 2836-1873
• DOI: 10.1109/ICCSP64183.2025.11089126

Managing urban waste today calls for innovative approaches that use advanced technologies. With predictive analytics, robotic sorting, and long short-term memory (LSTM) models, the research provides a waste management paradigm shift. Real-time bin and container fill monitoring is done by strategically placing Internet of Things (IoT) sensors across waste pickup sites. These sensors track waste generation patterns throughout time. With information, LSTM-based prediction algorithms can reliably predict when garbage cans are full. LSTM models are trained in past data to grasp complicated waste generation and accumulation processes. The framework integrates robotic sorting technologies into the waste management ecosystem to further enhance recycling efficiency. Robots equipped with sophisticated sensors, cameras, and ML algorithms can now sort recyclable waste autonomously. The proposed system is environmentally adaptive. LSTM models are retrained to provide accurate predictions using real-time IoT sensor data. The framework employs predictive analytics, robotic sorting, and IoT sensors for numerous benefits. Better waste collection routes and schedules, greater recycling rates, fewer operating costs, and less environmental effects.