Sustainable Solid Waste Segregation Through IoT and Linear Regression

The increasing difficulties in solid waste management have rendered old approaches ineffective in tackling the intricacies of waste categorization and disposal. Inefficient manual waste segregation results in environmental degradation and resource loss the Internet of Things (IoT), utilising Linear Regression (LR), predicts and enhances waste management. This work introduces a predictive modeling methodology for sustainable solid waste segregation using the IoT and LR analysis. IoT sensors, including weight, temperature, and gas sensors, enable real-time waste data collection to analyze its composition and trends. LR, a prevalent statistical technique, estimates the link between many variables, including time, weather, waste characteristics, and the kind and amount of waste produced. The predictive model aims to improve waste segregation precision, streamline collection procedures, and reduce contamination levels in recycling streams. Integrating IoT technology with LR enhances data-driven decision-making, optimizing the efficiency of waste management systems and fostering sustainable practices. This study illustrates how predictive modeling may revolutionize waste management, enhance environmental sustainability, and optimize resource allocation. IoT-based waste management model precisely predicts segregation requirements, achieving an $\mathbf{L R}$ model $\mathbf{R}^{\mathbf{2}}$ of $\mathbf{9 2 \%}$, improving efficiency and sustainability.