Models for Predicting Global Plastic Waste

Abstract

For more than half a century, plastic prod-ucts have been a part of people’s lives. When plastic waste is thrown into nature, it can cause a sequence of dangerous effects. Previous researchers esti-mated that global plastic waste in 2020 will be more than 400 million tons. To reduce plastic waste, they built scientific models to analyze the sources of plas-tic and provided solutions for regenerating these plastic wastes. However, their models are static and inaccurate, which may cause some false predictions.
In this paper, we first observe the distribution of the real-world plastic waste data. Then, we build simple exponential growth model and logistics model to match these data. By testing different models on our plots, we discover that the SELF-ADAPTIVE MODEL is the best to describe and correctly predict our future plastic waste production, as this model combines the benefits of SIMPLE EXPONENTIAL GROWTH MODEL and the LOGISTIC MODEL. The self-Adaptive model has the potential to minimize the error rate and make the predictions more accurate. Based on this model, we can develop more accurate and informative solu-tions for the real-world plastic problems.