Artificial intelligence for planning of energy and waste management

In the current era, waste management activities, including energy and material recycling, may create indirect environmental impacts beyond waste management systems. Energy waste is used to reproduce different products, such as electricity, heat, compost, and biofuels. Effective environmental protection depends on the quality of the information available for a proper decision. Reliable data collection is essential to facilitate planning processes in the effective planning of waste management. In this paper, the Machine learning-driven Predictive Analytic framework (MLDPAF) has been proposed to prepare energy and waste management. Firstly, with a neural network, the amount of waste is predicted. An enhanced machine learning algorithm further improves waste collection on energy costs based on volatile sustainable energy markets. Findings showed that proposed algorithms based on machine learning have been used successfully to generate efficient waste models. The simulation analysis shows that the analysis of waste quantity reduced by 90% using the proposed method, landfill analysis as 40%, and transportation reduced by 15%. © 2021