Intelligent Waste Management Optimization Through Machine Learning Analytics - Ogbolumani, Omolola A.; Adekoya, Muiz (Journal of Science Research and Reviews, 2025)
Waste management presents serious obstacles to making metropolitan regions more habitable. Conventional waste management techniques are not usually optimized, resulting in overflowing bins, wasteful waste collection trips, and various negative environmental effects. This study addresses these challenges by developing an intelligent system integrating the Internet of Things (IoT) and machine learning technologies. This study aims to develop an intelligent waste management system that optimizes waste collection routes and schedules through machine learning. (ML) models and Internet of Things (IoT) powered smart bins. The system utilized Support Vector Machines (SVM) and Artificial Neural Networks (ANN) for data analysis, complemented by dynamic route optimization algorithms. Data collection over 90 days across 47 sites encompassed bin fill levels, battery status, and environmental parameters such as temperature and humidity. Results demonstrated significant operational improvements, with the system achieving 89% accuracy in fill-level prediction and enabling a 35% reduction in collection frequency. Implementation led to a 42% decreased fuel consumption and a 2.4-hour reduction in daily collection times. Commercial zones exhibited 1.8 times higher fill rates than residential areas, while weekend waste generation peaked at 2.1 times weekday. The findings indicate that IoT-ML technology integration substantially enhances urban waste management efficiency through data-driven decision-making. Phased implementation, prioritizing high-waste-volume areas, integrating with existing metropolitan systems, and developing standardized data protocols are recommended. This research contributes to the growing body of evidence supporting smart technology adoption in urban waste management, offering a scalable solution for improved operational efficiency and environmental sustainability.
Keywords: Waste management; Internet of Things; Machine learning; Smart cities; Route optimization; Environmental sustainability