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Prophet time series modeling of waste disposal rates in four North American cities.
- Source :
-
Environmental science and pollution research international [Environ Sci Pollut Res Int] 2024 May; Vol. 31 (21), pp. 31343-31354. Date of Electronic Publication: 2024 Apr 17. - Publication Year :
- 2024
-
Abstract
- In this study, three different univariate municipal solid waste (MSW) disposal rate forecast models (SARIMA, Holt-Winters, Prophet) were examined using different testing periods in four North American cities with different socioeconomic conditions. A review of the literature suggests that the selected models are able to handle seasonality in a time series; however, their ability to handle outliers is not well understood. The Prophet model generally outperformed the Holt-Winters model and the SARIMA model. The MAPE and R <superscript>2</superscript> of the Prophet model during pre-COVID-19 were 4.3-22.2% and 0.71-0.93, respectively. All three models showed satisfactory predictive results, especially during the pre-COVID-19 testing period. COVID-19 lockdowns and the associated regulatory measures appear to have affected MSW disposal behaviors, and all the univariate models failed to fully capture the abrupt changes in waste disposal behaviors. Modeling errors were largely attributed to data noise in seasonality and the unprecedented event of COVID-19 lockdowns. Overall, the modeling errors of the Prophet model were evenly distributed, with minimum modeling biases. The Prophet model also appeared to be versatile and successfully captured MSW disposal rates from 3000 to 39,000 tons/month. The study highlights the potential benefits of the use of univariate models in waste forecast.<br /> (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Details
- Language :
- English
- ISSN :
- 1614-7499
- Volume :
- 31
- Issue :
- 21
- Database :
- MEDLINE
- Journal :
- Environmental science and pollution research international
- Publication Type :
- Academic Journal
- Accession number :
- 38632194
- Full Text :
- https://doi.org/10.1007/s11356-024-33335-5