A take on Smart Waste Management: Predictive Cost Analysis Using IoT and Machine Learning

Abstract

The rapid urbanization and population growth have led to an increase in municipal waste generation, posing significant challenges for waste management systems. Efficient waste management is crucial for environmental sustainability and public health. This research aims to develop a predictive model for smart waste management costs using Machine learning and IoT devices. Data were collected from IoT-enabled waste bins equipped with sensors to measure fill levels, weight, and environmental conditions. The data underwent preprocessing steps including handling missing values, outlier detection, and noise reduction. Key features were engineered and selected for model training, and various machine learning models, including Linear Regression, Decision Trees, Random Forests, and Neural Networks, were evaluated. The Random Forest model demonstrated the highest predictive accuracy with an MAE of 5.2, RMSE of 7.8, and R² of 0.89. The integration of IoT devices enabled real-time monitoring and dynamic scheduling, leading to potential cost savings, improved operational efficiency, and reduced environmental impact. The findings highlight the transformative potential of IoT applications in urban waste management, offering significant improvements in efficiency, cost savings, and sustainability. This study provides a foundation for future research to further optimize waste management systems and contribute to smarter, more efficient urban environments.