Abstract
The public's attention has been drawn to the severe increase of plastic waste. Researchers from many sectors are working to develop various solutions for plastic waste treatment and management. Exact forecasting and estimation of collected plastic waste helps decision makers to take efficient policies for recycling, treatment or disposal of different types of plastic waste. Estimation is an important tool for making informed decisions. Regardless of the size and profile of the company, forecasting helps the organization's management predict the trend of important indicators. Different statistical algorithms and machine learning methods are utilized to assess the future trends in light of historical data and current patterns. In this chapter, different regression models and autoregressive integrated moving average (ARIMA) techniques are used to forecast the collected plastic waste by 2050.
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Gharibi, A.R., Babazade, R., Hasanzadeh, R. (2023). Collected Plastic Waste Forecasting by 2050. In: Hasanzadeh, R., Mojaver, P. (eds) Plastic Waste Treatment and Management. Engineering Materials. Springer, Cham. https://doi.org/10.1007/978-3-031-31160-4_2
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DOI: https://doi.org/10.1007/978-3-031-31160-4_2
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