Maximizing Efficiency in Landfill Operations Using IoT and Random Forest Algorithms for Green Revolution

This paper proposes a new approach to waste management that is more sustainable and eco-friendly. Important landfill factors including temperature, humidity, gas emissions, and waste accumulation may be monitored in real-time with this system, which combines the Random Forest algorithm with Internet of Things (IoT) technology. The IoT sensor has the potential to provide real-time data interchange and monitoring, while the Random Forest algorithm a machine learning approach might provide effective pattern identification and decision-making. The use of Random Forest in predictive modeling method for the creation of early warning systems that can combat potential issues such gas leaks, overfilling, and operational inefficiencies before they ever happen. Future possibilities and consequences of this technology convergence may also be covered in the paper, with an emphasis on the important factors to consider for the effective and scalable deployment of waste management systems. Privacy and security of data, development of infrastructure, cost-effectiveness, and involvement of stakeholders are all factors that may be taken into account. A performance indicator would be a prediction accuracy of more than 95% on the validation and test datasets. Wind turbines and solar panels, which produce power from waste gas, may be more easily integrated into landfill operations using this technology, significantly reducing the environmental impact of landfill operations.