AI Based Prediction Algorithms for Enhancing the Waste Management System: A Comparative Analysis

Abstract

Waste management has become an increasingly pressing issue due to urbanization, population growth, and economic development. According to World Bank projections, waste production will reach 3.4 billion tonnes by 2050. The paper is focused on detailed analysis of waste management techniques that has to be improved and resources to be maximized, to be able to deal with various types of waste, including agricultural waste, industrial waste, municipal solid waste (MSW), and electronic waste (e-waste). The advancement in the artificial intelligence in various fields has drawn the attention towards utilizing its benefits in achieving optimized management of different types of wastes also. The paper is focused on description of on-recyclable waste materials which can be transformed into energy by using waste-to-energy (WTE) technologies. The different types of wastes generated in different sectors are being studied with details on their quantity and challenges in handling the wastes. The literature highlights the performance analysis of various methodologies of waste handling in terms of their efficiency, economic impacts and ecological implications. The prediction models and their performance was discussed with respect to the R2 value and mean absolute error (MAE) root mean square error (RMSE) to find the most suitable algorithm. The conclusion suggested that these AI based optimization methods can bring about enhancement in the various waste to energy conversion process making the management of waste materials more sustainable and reliable.

Keywords

• waste-management strategies
• waste sorting
• artificial intelligence
• prediction models
• rmse
• mae